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Skills Self-Assessment

Role: Computer Vision Engineer

Assessed: 0 Total: 0

AI Coding Assistants 1

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Understands basic GitHub Copilot usage for CV work: leveraging suggestions for image processing pipeline code, using Copilot Chat for PyTorch/OpenCV API questions, accepting completions for data augmentation and preprocessing functions. Follows team guidelines for reviewing AI-generated ML code.

Independently configures GitHub Copilot for CV workflows: crafts prompts for model architecture code and training loops, uses Copilot Chat for PyTorch/TensorFlow debugging, evaluates suggestion accuracy for numerical computing patterns. Understands trade-offs between Copilot speed and ML code correctness for training pipelines.

Designs GitHub Copilot adoption strategies for CV teams: optimizes prompt engineering for model architecture and training pipeline code, implements validation for AI-generated numerical computing patterns, evaluates Copilot accuracy for domain-specific CV algorithms. Mentors team on effective AI-assisted CV development practices.

Defines GitHub Copilot strategy for CV teams: establishes guidelines for AI-assisted model development, designs validation workflows for AI-generated numerical computing code, drives adoption of Copilot best practices for training pipeline and inference code.

Algorithms & Data Structures 2

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Understands basic algorithmic concepts for CV: simple image filtering operations, basic sorting for data preprocessing, linear search through datasets. Follows team guidance on algorithm selection for CV pipeline tasks.

Independently applies algorithmic thinking in CV: selects appropriate image processing algorithms for preprocessing, understands convolution and pooling operation complexity, evaluates model inference optimization strategies. Analyzes computational costs of different data augmentation pipelines.

Applies algorithmic expertise to computer vision: image processing algorithm optimization (convolution, morphological operations), efficient feature extraction algorithms, non-maximum suppression for object detection post-processing. Designs real-time inference algorithms balancing accuracy and latency constraints.

Defines algorithmic standards for CV team: image processing algorithm selection criteria, inference optimization benchmarks, data augmentation algorithm evaluation. Conducts reviews of algorithmic decisions in real-time CV pipelines.

Understands basic data structures for CV work: numpy arrays for image data representation, lists for batch processing, dictionaries for annotation metadata. Applies simple data structures following team patterns for data loading and preprocessing tasks.

Independently selects appropriate data structures for CV tasks: numpy arrays for image data, pandas DataFrames for annotation management, tensor formats for model input/output. Understands trade-offs between data storage formats for training data loading performance.

Selects optimal data structures for computer vision systems: k-d trees for spatial nearest neighbor search, octrees for 3D point cloud processing, efficient tensor layouts for GPU batch processing. Optimizes image data pipelines with memory-mapped arrays and prefetch buffers. Designs annotation storage structures for large-scale dataset management.

Defines data structure standards for CV team: annotation schema design, training dataset organization, inference data pipeline conventions. Conducts reviews of CV data architecture decisions. Establishes team guidelines for efficient image data handling and model input formatting.

Background Jobs & Task Queues 1

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Uses Task Queues at a basic level in PyTorch/OpenCV. Performs simple tasks using ready-made templates. Understands basic concepts and follows team practices.

Independently implements tasks with Task Queues in PyTorch/OpenCV. Understands internals and optimizes performance. Writes tests using evaluation metrics.

Designs task queue systems for computer vision pipelines: distributed image/video processing, GPU-aware job scheduling, and result aggregation. Implements retry strategies and dead letter queues for handling processing failures.

Defines task queue architecture for large-scale vision processing: multi-stage pipelines with fan-out/fan-in patterns, resource-aware scheduling across GPU clusters, and SLA-driven priority management for real-time vs batch workloads.

Clean Code & Refactoring 1

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Understands basic code quality principles applied to Python/OpenCV development. Follows team naming conventions and code formatting rules. Writes simple, readable functions for image processing tasks. Accepts code review feedback and applies corrections.

Independently applies code quality practices in computer vision development. Writes clean training and inference pipeline code with proper data handling. Understands trade-offs between model experimentation speed and code structure. Reviews CV code for data augmentation correctness, preprocessing consistency, and reproducibility.

Designs code quality standards for computer vision pipelines: model training code structure, inference pipeline clarity, data preprocessing modularity. Refactors research prototypes into production-ready CV systems with proper error handling and logging. Implements quality gates for model serving code.

Defines code quality standards for CV engineering team: training pipeline code conventions, model experiment documentation, inference code review practices. Conducts architectural reviews of CV system designs. Establishes quality gates for model deployment readiness.

Code Review 1

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Understands basic code review for CV pipelines: reviewing image preprocessing changes, checking model inference code modifications, validating data augmentation logic. Follows team review guidelines for CV codebases.

Independently conducts code reviews for CV pipelines: evaluates image processing algorithm implementations, reviews model inference code efficiency, checks data augmentation correctness. Provides constructive feedback on CV pipeline architecture and performance patterns.

Designs code review processes for CV pipelines: image processing code review standards, model inference performance review gates, data augmentation correctness checklists. Mentors team on reviewing CV code for both correctness and efficiency.

Defines code review strategy for CV team: image processing code review standards, model inference review governance, CV pipeline review practices. Establishes review culture for CV code quality and performance.

Computer Vision 3

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Concurrency & Parallelism 1

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Understands the fundamentals of Multithreading at a basic level. Applies simple concepts in work tasks using Python. Follows recommendations from senior developers when solving problems.

Independently applies multithreading for CV work: parallel data augmentation and preprocessing pipelines, multi-GPU training with PyTorch DataParallel/DistributedDataParallel, concurrent image loading with DataLoader workers. Explains concurrency trade-offs for training pipeline performance.

Has deep expertise in multithreading for CV: designs multi-GPU training architectures with efficient data parallelism, implements concurrent inference pipelines for real-time CV applications, optimizes memory management across GPU/CPU boundaries. Mentors team on high-performance parallel computing for computer vision.

Defines multithreading standards for CV teams: establishes guidelines for multi-GPU training architectures, conducts reviews of concurrent inference pipeline designs, creates training materials on parallel computing patterns for computer vision applications.

gRPC 1

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Understands basics of gRPC & Protocol Buffers for computer vision service communication. Reads proto definitions for image processing request/response schemas. Follows team conventions for defining vision model inference service contracts and streaming image data.

Independently designs gRPC services for computer vision pipelines — streaming image data with bidirectional gRPC, efficient proto schemas for bounding box and segmentation results, and batched inference request patterns. Writes comprehensive proto documentation and integration tests for vision service contracts.

Designs gRPC API architecture for production computer vision platforms — multi-model inference service mesh with efficient proto schemas, video streaming pipelines with flow control, and versioned vision service contracts supporting model evolution. Defines error handling standards for vision-specific failures and optimizes proto serialization for high-throughput image processing.

Defines gRPC API strategy for computer vision service portfolios — establishes proto governance for vision model serving contracts, designs cross-team schema evolution policies for vision pipeline APIs, and coordinates standardized streaming patterns for image/video processing services.

Integration Testing 1

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Understands basics of integration testing for computer vision pipeline stages. Writes simple tests verifying image preprocessing, model inference, and postprocessing data flow. Follows team testing guidelines for camera/sensor input integration validation.

Independently designs integration tests for end-to-end computer vision pipelines from image capture to inference output. Applies contract testing for model serving API boundaries. Integrates visual regression and accuracy validation tests into CI/CD.

Designs integration testing strategy for multi-stage computer vision systems including camera calibration, preprocessing, and multi-model inference. Implements automated visual quality validation across the processing pipeline. Optimizes test pyramid balancing model accuracy tests with hardware integration checks. Mentors team on testing real-time CV system performance boundaries.

Defines integration testing strategy for production computer vision systems across edge devices and cloud processing. Establishes quality standards for vision pipeline integration points including accuracy benchmarks and latency SLAs. Implements shift-left testing culture with early validation of camera specifications and model compatibility matrices.

MLOps 2

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Logs computer vision experiments in MLflow, tracking metrics like mAP, IoU, and loss curves. Registers trained model artifacts (YOLO, ResNet checkpoints) and tags runs with dataset versions. Follows team conventions for organizing CV experiment naming and parameter logging.

Designs MLflow experiment structures for multi-stage CV pipelines: augmentation tuning, backbone selection, and detection head optimization. Implements custom metrics logging for CV tasks (dice scores, MOTA). Configures Model Registry for versioning production CV models.

Architects MLflow tracking for large-scale CV research: distributed GPU training, experiment lineage for distillation chains, and automated model promotion on COCO/ImageNet benchmarks. Builds custom MLflow plugins for CV-specific artifacts like annotation overlays and inference visualizations.

Defines MLflow strategy at the team/product level. Establishes standards and best practices. Conducts reviews.

Understands basic model monitoring concepts for computer vision pipelines. Tracks prediction accuracy, image quality drift, and inference latency using dashboards. Follows team guidelines for setting up alerts on object detection and classification models in staging environments.

Independently configures model monitoring for computer vision systems — tracks distribution shifts in input images, bounding box accuracy degradation, and segmentation quality. Sets up Evidently or Prometheus-based dashboards for real-time detection of concept drift across visual inference pipelines.

Designs production-grade monitoring systems for computer vision at scale. Implements automated detection of dataset shift, label quality degradation, and model staleness across multi-model serving architectures. Optimizes monitoring overhead for high-throughput video and image processing pipelines. Mentors team on SLO-driven alerting strategies.

Defines Model Monitoring strategy at the team/product level. Establishes standards and best practices. Conducts reviews.

Relational Databases 1

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Understands basic PostgreSQL for CV work: storing image metadata and annotation data, basic queries for dataset management, understanding blob storage patterns for image references. Follows team conventions for CV dataset schema design and query patterns.

Independently designs database schemas for CV pipelines: optimizes queries for annotation data retrieval, manages large-scale image metadata storage, implements efficient dataset versioning patterns. Understands trade-offs between PostgreSQL and object storage for CV artifact management.

Designs PostgreSQL architecture for CV data infrastructure: optimizes database for large-scale annotation management and dataset versioning, implements efficient metadata search across millions of images, configures partitioning for training data lifecycle management. Mentors team on database patterns for computer vision data pipelines.

Defines PostgreSQL data strategy for CV teams: establishes standards for image metadata and annotation storage, designs database architecture for dataset management at scale, drives adoption of PostgreSQL best practices for computer vision data pipelines.

REST API 1

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Understands basic REST API concepts for CV services: image upload endpoints, inference result response formats, batch processing API patterns. Follows team conventions for model serving API design in computer vision projects.

Independently designs REST APIs for CV services: image/video upload endpoints with multipart handling, async inference APIs for long-running CV tasks, batch prediction endpoints. Understands best practices for large binary payload handling and streaming results in CV APIs.

Designs API architecture for CV services: high-throughput image processing endpoints, async inference API with webhook callbacks, streaming video analysis APIs. Defines API contracts for CV pipeline components. Mentors team on optimizing API design for large binary payloads and real-time CV.

Defines API strategy for CV services at product level: inference API performance standards, image/video processing API governance, model serving endpoint lifecycle policies. Conducts API architecture reviews for CV platform services.

Web Frameworks 1

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Uses Python Web Frameworks at a basic level in PyTorch/OpenCV. Performs simple tasks using ready-made templates. Understands basic concepts and follows team practices.

Independently implements tasks with Python Web Frameworks in PyTorch/OpenCV. Understands internals and optimizes performance. Writes tests using evaluation metrics.

Designs computer vision inference APIs using FastAPI with async image processing pipelines. Optimizes streaming endpoints for real-time video analysis. Selects frameworks balancing throughput and memory for large model payloads. Mentors the team on efficient media handling.

Defines API architecture for computer vision platforms serving image and video processing pipelines via FastAPI. Establishes standards for media upload handling, batch inference endpoints, and result streaming. Conducts design reviews and defines the roadmap for CV service scalability.

Additional skills (optional) 33

These skills do not affect the main grade but show the breadth of your profile.

AI Coding Assistants

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Uses ChatGPT/Claude for generating image processing code snippets and understanding computer vision paper abstracts. Applies AI assistants to explore model architectures and debug preprocessing pipelines. Follows team guidelines for validating AI-generated vision code against accuracy benchmarks.

Independently applies ChatGPT/Claude for computer vision development — generating augmentation pipelines, debugging model training issues, and creating evaluation scripts. Understands trade-offs between AI-assisted and manual approaches for vision system components. Designs effective prompts for image processing and model optimization tasks.

Has deep expertise in applying ChatGPT/Claude for computer vision system development. Designs AI-assisted workflows for model architecture search, training pipeline optimization, and vision system documentation. Optimizes prompt engineering for complex vision tasks and mentors team on leveraging LLMs for accelerating CV research and development.

Defines ChatGPT/Claude adoption strategy for computer vision engineering teams. Establishes standards for AI-assisted model development, research acceleration workflows, and prompt engineering for vision tasks. Conducts reviews of LLM integration patterns in CV development ensuring quality and reproducibility.

Familiar with AI-oriented IDEs like Cursor and can use them for writing image processing code. Understands basic AI autocompletion capabilities.

Effectively uses Cursor for CV pipeline development — augmentation generation, writing training loops, model debugging. Configures AI context for ML projects.

Optimizes Cursor workflow for CV development, creates custom rules for ML projects. Integrates AI assistant with experiment tracking and model registry.

Defines AI IDE usage standards for the CV team, measures impact on productivity. Trains developers in effective AI-assisted ML development techniques.

API Management

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Understands the importance of API documentation for CV services and can read OpenAPI specifications for inference endpoints. Knows basic documentation tools.

Creates documentation for CV API — inference endpoints, model versioning, input/output formats. Describes model limitations, supported image formats.

Designs comprehensive CV platform documentation — API reference, model cards, data sheets. Automates documentation generation from code and model metadata.

Defines CV API documentation standards for the team, implements model cards and dataset documentation. Organizes developer portal for CV service consumers.

Application Security

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Familiar with key OWASP Top 10 vulnerabilities and understands their impact on CV services. Avoids injections when processing user images and metadata.

Applies OWASP recommendations when developing CV APIs — input image validation, adversarial attack protection, secure model storage. Configures rate limiting.

Conducts security analysis of CV systems using OWASP methodology, including protection against model extraction and data poisoning. Implements SAST/DAST for ML services and inference endpoints.

Defines CV application security policies based on OWASP guidelines, including model intellectual property protection. Organizes ML infrastructure security audits.

Follows basic secure coding practices — input image validation, secure model storage. Does not store secrets and GPU access keys in code.

Applies secure by design principles when developing CV services, including input data sanitization and model artifact protection. Uses SAST for Python/C++ code.

Implements comprehensive security practices in CV projects — model signing, secure inference, adversarial input protection. Develops guidelines for secure data handling.

Defines secure coding standards for the CV team, organizes security reviews of ML code. Coordinates model protection as intellectual property.

Batch Processing

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Understands Pandas basics for CV data pipelines: image metadata management in DataFrames, annotation dataset loading and manipulation, and dataset splitting for training/validation/test sets. Follows team practices for data preprocessing and augmentation pipeline input preparation.

Implements CV data management pipelines with Pandas: annotation format conversion (COCO, YOLO, VOC), dataset statistics computation for class imbalance analysis, and batch metadata processing for training monitoring. Integrates Pandas with image processing libraries for efficient data loading. Optimizes DataFrame operations for large annotation datasets.

Designs data management architecture for CV systems with Pandas/Polars: automated dataset versioning, cross-dataset analysis pipelines, and model performance tracking data infrastructure. Implements efficient annotation management for million-scale datasets. Creates data quality frameworks for training data validation. Mentors team on efficient data processing for CV workflows.

Defines data strategy for CV engineering teams. Shapes data platform for computer vision: dataset management tools, annotation pipeline standards, and data quality governance for training data. Coordinates teams on data sharing practices and cross-project dataset reuse. Drives adoption of efficient data processing tools.

Caching

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Uses Redis at a basic level in PyTorch/OpenCV. Performs simple tasks using ready-made templates. Understands basic concepts and follows team practices.

Independently implements tasks with Redis in PyTorch/OpenCV. Understands internals and optimizes performance. Writes tests using evaluation metrics.

Designs Redis caching architectures for computer vision pipelines — image feature vector caching for similarity search, model inference result caching for repeated image queries, and distributed task queue management for batch processing. Optimizes Redis data structures for high-throughput vision data. Mentors team on caching strategies for real-time vision applications.

Defines Redis caching strategy for computer vision platforms — establishes standards for image feature caching, model result TTLs, and batch processing queue management. Conducts design reviews of vision pipeline caching and defines technical roadmap for real-time vision data infrastructure.

CI/CD

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Understands basic GitHub Actions for CV work: running model training validation pipelines, reading CI logs for test failures in CV code, understanding workflow triggers for dataset processing jobs. Follows team conventions for ML pipeline CI automation.

Configures CI/CD pipelines for automatic training and validation of computer vision models. Configures testing with GPU runners and dataset caching.

Designs complex CI/CD workflows for MLOps processes — automatic training, evaluation, model registry. Integrates DVC, MLflow, and model quality checks.

Defines CI/CD standards for the CV team, creates reusable actions for ML pipelines. Optimizes GPU resource costs and workflow execution time.

Classical Machine Learning

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Understands core scikit-learn estimators (SVM, Random Forest, KNN) for image feature classification. Applies basic pipelines with StandardScaler and PCA for visual feature preprocessing. Follows team conventions for cross-validation on image datasets.

Independently builds scikit-learn pipelines for HOG/SIFT feature extraction with SVM and ensemble classifiers for image recognition. Understands trade-offs between model complexity and inference speed. Tunes hyperparameters via GridSearchCV for optimal precision-recall.

Has deep expertise combining scikit-learn classical ML with deep learning feature extractors for hybrid CV systems. Designs scalable prediction services with joblib serialization and sparse matrix optimization. Mentors the team on validation strategies for imbalanced visual datasets.

Defines team strategy for integrating scikit-learn classical ML into the CV pipeline alongside deep learning. Establishes standards for model benchmarking and evaluation protocols across detection and classification tasks. Conducts reviews of ML pipeline architecture for reproducibility.

Cloud Providers

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Understands basic AWS concepts for computer vision workloads — S3 for image storage, EC2 GPU instances for training, and Rekognition for managed vision services. Uses ready-made configurations for GPU instance provisioning. Performs simple operations like uploading datasets to S3 and monitoring training jobs under senior guidance.

Independently configures AWS services for vision workloads — EC2 GPU instances with CUDA, S3 lifecycle policies for image datasets, and Lambda for lightweight inference. Writes Terraform/CDK for GPU training infrastructure patterns. Understands VPC networking for model serving security and spot instance management for cost-effective training.

Designs AWS infrastructure solutions for production computer vision systems — multi-GPU EC2 training clusters, SageMaker real-time inference endpoints, and S3-based image processing pipelines. Optimizes cost through spot instance strategies for training and right-sized inference endpoints. Implements security hardening for vision workloads with VPC isolation, model artifact encryption, and access controls for sensitive image data.

Defines AWS infrastructure strategy for computer vision platforms spanning GPU training clusters and inference deployments. Establishes IaC standards for vision workload provisioning and model deployment automation. Conducts architecture reviews optimizing GPU cost-performance ratio and drives FinOps for vision compute resources.

Computer Vision

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Concurrency & Parallelism

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Understands basic async programming in Python: asyncio for concurrent I/O, async data loading patterns for training pipelines, basic concurrent image processing concepts. Follows team conventions for async code in CV data loading.

Independently applies async programming in CV pipelines: concurrent image loading with async data loaders, non-blocking inference service handling, async result aggregation for batch processing. Understands trade-offs between sync and async patterns for GPU-bound CV workloads.

Designs async architectures for CV systems: async data loading pipelines for training, concurrent inference request handling, GPU-aware async scheduling. Mentors team on async patterns for CV pipeline throughput optimization.

Defines async programming standards for CV team: async data loading pipeline guidelines, concurrent inference architecture reviews, GPU-aware async scheduling patterns. Establishes best practices for async patterns in CV processing systems.

Containerization

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Understands basic Docker concepts for CV work: containerized model training with GPU passthrough, Docker images for OpenCV/PyTorch dependencies, basic docker-compose for inference service stack. Uses ready-made configurations for GPU-enabled CV containers.

Independently configures Docker for CV workloads: GPU-enabled training containers with CUDA, multi-stage builds for inference service images, docker-compose for CV pipeline development stack. Understands container networking for model serving and NVIDIA container toolkit configuration.

Designs Docker infrastructure for CV systems: GPU-optimized container builds with CUDA/cuDNN layers, inference service scaling architecture, container registry management for model versioning. Implements best practices for GPU memory management and multi-model serving containers.

Defines Docker infrastructure strategy for CV systems: GPU container platform governance, model serving container standards, inference scaling architecture reviews. Conducts architecture reviews for CV pipeline containerization and optimizes FinOps for GPU container workloads.

Database Optimization

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Understands indexing concepts and can create simple indexes for image metadata tables. Knows the impact of indexes on search speed in CV datasets.

Creates efficient indexes for CV data stores — composite indexes for annotation filtering, partial indexes for processing statuses. Uses EXPLAIN for analysis.

Designs indexing strategy for large CV datasets considering vector indexes for embeddings, GiST for geodata. Optimizes feature vector storage and search.

Defines indexing standards for CV team projects, ensures optimal query performance. Coordinates vector database selection and similarity search strategies.

Deep Learning

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Builds basic CNNs in PyTorch for image classification using torchvision pretrained models (ResNet, VGG). Writes custom Dataset and DataLoader for image data. Applies torchvision.transforms for augmentation: random crop, flip, color jitter. Understands training loop basics.

Implements detection (YOLO, SSD) and segmentation (U-Net, Mask R-CNN) in PyTorch. Fine-tunes pretrained models with custom heads and layer freezing. Builds augmentation pipelines with Albumentations. Processes video with efficient batching and temporal models.

Designs end-to-end CV systems in PyTorch: multi-task learning, knowledge distillation, custom losses for detection/segmentation. Optimizes inference with TorchScript, quantization and pruning. Builds real-time video pipelines with efficient GPU memory management.

Defines PyTorch strategy at the team/product level. Establishes standards and best practices. Conducts reviews.

Distributed Tracing

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Understands observability concepts and the role of OpenTelemetry for monitoring CV services in production. Can view inference request traces in Jaeger/Zipkin.

Implements OpenTelemetry in CV services for tracking inference latency, model loading time, and GPU utilization. Configures distributed tracing for multi-model pipelines.

Designs observability system for CV platform based on OpenTelemetry — custom metrics for ML, semantic conventions for inference. Implements tracing across training and serving.

Defines observability standards for the CV team based on OpenTelemetry, creates dashboards and alerting policies. Coordinates GPU resource and model quality monitoring.

File & Object Storage

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Uses S3 / Object Storage at a basic level in PyTorch/OpenCV. Performs simple tasks using ready-made templates. Understands basic concepts and follows team practices.

Independently implements tasks with S3 / Object Storage in PyTorch/OpenCV. Understands internals and optimizes performance. Writes tests using evaluation metrics.

Designs S3-based storage for large-scale image/video datasets and model artifacts. Implements versioned dataset pipelines with S3 Select for efficient querying. Optimizes multipart uploads for training data ingestion.

Defines architectural decisions for S3 / Object Storage at the product level. Establishes standards. Conducts design reviews and defines technical roadmap.

Git & Workflows

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Understands basic Git advanced features for CV work: branching for model experiment isolation, LFS for managing large training datasets and model weights, stash for switching between training experiments. Follows team conventions for versioning CV pipeline code alongside model artifacts.

Independently uses Git advanced features for CV projects: manages experiment branches with LFS for large model files, resolves conflicts in training configuration YAMLs, implements hooks for dataset integrity checks. Understands trade-offs between Git LFS and DVC for versioning large CV datasets.

Designs Git workflows for CV teams: implements LFS and DVC integration strategies for large dataset management, optimizes branching for parallel model training experiments, automates artifact versioning in CI pipelines. Mentors team on reproducible experiment tracking through Git-based workflows.

Defines Git strategy for CV teams: establishes standards for versioning models, datasets, and training configurations, designs repository architecture for shared CV components, drives adoption of LFS/DVC best practices for large artifact management across research and production teams.

GraphQL

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Understands basics of GraphQL schema design for computer vision service APIs. Writes simple queries for image processing task submission and result retrieval. Follows team conventions for defining types that represent vision model outputs and annotation data.

Designs GraphQL schemas for CV API — prediction queries, batch inference, model management. Implements subscriptions for real-time processing result updates.

Designs scalable GraphQL APIs for the CV platform with federation and custom scalars for images. Optimizes queries for batch inference and model management.

Defines GraphQL API standards for the CV team, creates guidelines for designing ML service schemas. Coordinates API contracts between CV and frontend teams.

Kubernetes & Orchestration

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Understands basic Kubernetes concepts for deploying computer vision inference services. Uses ready-made configurations for GPU-accelerated pods serving vision models. Performs simple operations like checking pod logs and scaling inference replicas under guidance.

Independently configures Kubernetes deployments for GPU-accelerated computer vision workloads. Writes Helm charts for multi-model inference services with resource quotas and node affinity rules. Understands pod networking for vision pipeline inter-service communication and GPU scheduling policies.

Designs Kubernetes infrastructure for production computer vision systems requiring GPU scheduling, model versioning, and real-time inference scaling. Implements cost-optimized GPU node pools with spot instances and preemptible workloads. Applies security hardening for vision model serving with network policies and image scanning.

Defines Kubernetes infrastructure strategy for computer vision workloads across GPU clusters. Establishes IaC standards for model deployment, GPU resource management, and inference autoscaling. Conducts architecture reviews optimizing cost-performance ratio for vision processing and coordinates FinOps for GPU compute.

Logging

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Understands the importance of structured logging for debugging CV pipelines and can set up basic logging with model context. Knows JSON log formats.

Implements structured logging in CV systems with experiment tracking, training metrics, and inference latency. Configures log correlation with MLflow/W&B.

Designs logging system for production CV services with APM integration, data drift tracking, and prediction quality monitoring. Optimizes log volume.

Defines logging standards for the CV team, creates libraries and templates for experiment tracking. Ensures result reproducibility through structured records.

Memory Management

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Understands the fundamentals of Memory Management at a basic level. Applies simple concepts in work tasks using Python. Follows recommendations from senior developers when solving problems.

Manages memory for image and video processing pipelines: buffer allocation, zero-copy operations, and GPU memory transfers. Understands OpenCV Mat memory layout and reference counting. Handles large tensor allocations for inference models. Profiles memory usage in real-time processing scenarios.

Designs memory architectures for production CV systems: GPU memory pool management, unified memory for CPU-GPU data sharing, and streaming processing for high-resolution video. Implements custom allocators for real-time inference pipelines. Optimizes model loading and weight sharing across multiple inference streams. Mentors team on GPU memory debugging (cuda-memcheck, nsight) and allocation profiling.

Defines memory management standards for CV/ML engineering teams. Establishes GPU memory governance: allocation policies, memory pool sizing, and OOM prevention strategies. Conducts architectural reviews of memory-critical pipelines. Creates training materials on GPU/CPU memory optimization for the team.

Metrics & Monitoring

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Understands the purpose of Prometheus and Grafana for monitoring and can view ready-made dashboards with CV service metrics. Knows basic ML metric types.

Configures CV service monitoring in Prometheus — inference latency, throughput, GPU utilization, accuracy drift. Creates Grafana dashboards for tracking model quality.

Designs production CV model monitoring system with data drift detection, quality metric degradation alerting. Implements A/B model testing through metrics.

Defines CV system monitoring standards for the team, creates dashboard templates and SLI/SLO for inference services. Ensures unified ML production health view.

MLOps

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Logs basic experiment parameters and metrics (accuracy, loss) using MLflow or W&B under supervision; follows team conventions for run naming and tagging in computer vision projects

Independently structures experiment tracking for CV pipelines: versions datasets with DVC, logs augmentation configs and model architectures in W&B, and builds comparison dashboards for detection/segmentation metrics

Designs end-to-end experiment tracking architecture for CV teams: integrates MLflow with CI/CD for automated metric collection, establishes artifact versioning for large image datasets, and mentors engineers on reproducible experiment practices

Defines experiment tracking strategy for CV teams: standardizes tracking of dataset evolution, annotation quality metrics, and model performance across deployment targets; ensures experiment lineage supports regulatory compliance for safety-critical vision systems

Understands ML pipeline stages for computer vision: data loading, augmentation, training, and evaluation. Runs existing pipelines and tracks experiment metrics using MLflow or Weights & Biases.

Builds end-to-end ML pipelines for vision models: dataset versioning, distributed training, hyperparameter tuning, and model registry integration. Implements data quality checks for image datasets.

Designs scalable ML pipeline architectures for large-scale vision systems: multi-GPU training orchestration, model distillation workflows, and production inference pipelines with latency optimization.

Defines ML Pipelines strategy at the team/product level. Establishes standards and best practices. Conducts reviews.

Understands model serving basics for CV systems: image/video inference pipeline setup, GPU resource allocation for inference, and model format conversion (ONNX, TensorRT). Follows team practices for deploying CV models to production endpoints.

Implements CV model serving pipelines: batch and real-time inference with GPU optimization, model ensemble strategies for accuracy improvement, and pre/post-processing pipeline optimization. Configures TensorRT/ONNX Runtime for inference acceleration. Implements model caching and warm-up strategies for consistent latency.

Designs CV model serving architecture: edge-cloud hybrid inference for latency-critical applications, multi-GPU serving with dynamic batching, and model distillation pipelines for deployment optimization. Implements serving monitoring: inference latency, GPU utilization, and prediction accuracy tracking. Creates reference architectures for CV model deployment. Mentors team on production CV system design.

Defines model serving strategy for CV engineering teams. Establishes inference performance standards, GPU resource governance, and model deployment pipelines. Conducts architecture reviews for CV serving infrastructure. Drives adoption of optimized inference patterns for production CV systems.

Networking

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Understands basic network concepts and can diagnose connection issues to GPU servers and data stores. Knows TCP/IP and HTTP basics for APIs.

Understands CV infrastructure network architecture, optimizes large image volume transfers. Configures GPU cluster connections via VPN and SSH.

Designs CV system network architecture considering high-bandwidth data transfer, GPU interconnect, and model serving. Optimizes latency for real-time inference.

Defines network requirements for the team's CV infrastructure, coordinates GPU cluster setup with DevOps. Ensures reliable data transfer between training and serving.

OOP & Design Patterns

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Knows basic design patterns and can recognize Factory, Strategy in CV pipeline code. Understands pattern application for organizing image processing code.

Applies Pipeline, Strategy, and Builder patterns for building flexible CV image processing pipelines. Uses Registry pattern for managing models and augmentations.

Designs CV system architecture using complex patterns — Plugin, Chain of Responsibility, Mediator. Creates extensible frameworks for model experimentation.

Defines architectural pattern catalog for the CV team, trains engineers in proper pattern selection. Conducts architectural reviews focused on ML system extensibility.

Understands basic OOP concepts in Python: classes, inheritance, abstract methods. Applies simple SOLID principles when structuring CV pipeline code. Follows team patterns for image processor classes and model inference wrapper design in PyTorch/OpenCV projects.

Independently applies OOP/SOLID in CV pipeline code: proper abstraction for image preprocessing stages, interface-based model inference backends, single responsibility in data augmentation modules. Understands trade-offs between class-based pipeline design and functional transforms in PyTorch/OpenCV.

Applies OOP/SOLID in CV system architecture: strategy pattern for swappable model backends, abstract pipeline interfaces for preprocessing/postprocessing stages, dependency injection for dataset/augmentation providers. Designs extensible CV frameworks that allow adding new model architectures without changing pipeline code.

Defines OOP/SOLID standards for CV team: image processing pipeline abstraction guidelines, inference backend interface contracts, data augmentation module architecture. Conducts reviews of class design decisions in real-time CV processing systems.

Prompt Engineering

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Knows prompt engineering basics and can use AI assistants for generating image processing and CV pipeline code. Understands query formulation principles.

Applies prompt engineering to accelerate CV model development — augmentation generation, architectures, data pipelines. Uses vision-language models for data analysis.

Develops AI usage standards for CV development, creates prompts for automating routine tasks. Integrates multimodal models into CV pipelines.

Defines AI-assisted CV development strategy for the team, measures prompt engineering effectiveness. Creates a prompt library for standard CV tasks.

System Design

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Understands basic system design concepts and can draw a simple CV pipeline diagram. Knows client-server architecture for inference services.

Designs architecture of medium CV systems considering batch and real-time inference, model and data storage. Selects appropriate technologies for specific CV tasks.

Designs distributed CV systems for processing millions of images considering GPU clusters, model serving, and data pipelines. Optimizes latency and throughput.

Leads CV system design in the team, conducts design reviews and defines architectural standards. Balances GPU infrastructure costs and performance requirements.

Type Systems

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Understands type safety concepts and can use type hints in Python for CV code. Knows the benefits of typing for preventing errors in ML pipelines.

Applies strict typing in CV projects with mypy/pyright, creates typed dataclasses for model input/output. Ensures type safety in inference API.

Designs type-safe CV system architecture with generic types for various models and transformations. Implements runtime validation with pydantic and compile-time checks.

Defines typing standards for the CV team, ensures consistency in ML code. Integrates type checking into CI/CD and organizes legacy code migration to typed Python.

Unit Testing

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Understands basic unit testing for CV code: pytest for image processing functions, test fixtures with sample images, assertion patterns for tensor shapes and pixel values. Follows team practices for testing CV pipeline stages independently.

Independently writes unit tests for CV pipelines: pytest for image preprocessing and augmentation functions, deterministic test fixtures for tensor operations, edge case coverage for input resolution/format variations. Integrates CV tests into CI/CD with GPU/CPU compatibility checks.

Designs testing strategy for CV pipelines: test pyramid for image processing stages, visual regression testing for model outputs, property-based tests for augmentation invariants. Optimizes test execution with GPU-aware test parallelization. Mentors team on reproducible CV testing.

Defines testing strategy at product level for CV systems: visual regression testing standards, model accuracy testing governance, inference performance testing frameworks. Establishes shift-left testing culture for CV pipelines with automated quality gates for model deployments.