These skills do not affect the main grade but show the breadth of your profile.
AI Coding Assistants
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Understands the fundamentals of Claude Code / Agentic Coding. Applies basic practices in daily work. Follows recommendations from the team and documentation.
Independently applies Claude Code / Agentic Coding in practice. Understands trade-offs of different approaches. Solves typical tasks independently.
Has deep expertise in Claude Code / Agentic Coding. Designs solutions for production systems. Optimizes and scales. Mentors the team.
Defines Claude Code / Agentic Coding strategy at the team/product level. Establishes standards and best practices. Conducts reviews.
Understands the fundamentals of Cursor IDE. Applies basic practices in daily work. Follows recommendations from the team and documentation.
Independently applies Cursor IDE in practice. Understands trade-offs of different approaches. Solves typical tasks independently.
Has deep expertise in Cursor IDE. Designs solutions for production systems. Optimizes and scales. Mentors the team.
Defines Cursor IDE strategy at the team/product level. Establishes standards and best practices. Conducts reviews.
Algorithms & Data Structures
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Understands the fundamentals of Algorithms & Complexity at a basic level. Applies simple concepts in work tasks using Python/TypeScript. Follows recommendations from senior developers when solving problems.
Independently applies algorithmic thinking in AI product development: evaluates training algorithm efficiency, understands inference optimization trade-offs, selects appropriate search/ranking algorithms for product features. Analyzes computational costs of ML pipeline operations.
Applies algorithmic expertise to AI product optimization: training algorithm convergence analysis, inference latency optimization through algorithmic approximation, efficient search algorithms for recommendation and retrieval systems. Evaluates computational complexity trade-offs in ML pipeline design.
Defines algorithmic standards for AI product team: model training algorithm selection criteria, inference optimization strategies, search/ranking algorithm evaluation frameworks. Conducts reviews of ML pipeline algorithmic efficiency.
Understands the fundamentals of Data Structures at a basic level. Applies simple concepts in work tasks using Python/TypeScript. Follows recommendations from senior developers when solving problems.
Independently selects appropriate data structures for AI products: feature arrays for model input, batch containers for training data, result caches for prediction serving. Understands trade-offs between data format choices for ML pipeline efficiency.
Selects optimal data structures for AI product systems: embedding indices, feature stores with efficient lookup, batch vs streaming data containers for ML pipelines. Optimizes tensor data layouts for training and inference throughput. Designs caching structures for model prediction results.
Defines data structure standards for AI product team: feature store schema design, model input/output format conventions, training data pipeline structures. Conducts reviews of ML data architecture decisions. Establishes team guidelines for efficient data handling in ML systems.
API Management
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Understands basics of API documentation for ML model serving endpoints and experiment tracking services. Reads and follows existing API docs for inference APIs and feature stores. Documents simple request/response schemas for AI product integration points.
Independently creates API documentation for ML model serving endpoints and experiment APIs. Writes integration guides for inference services with request/response examples and SDK usage patterns. Documents model versioning, feature flag configurations, and A/B test API contracts.
Designs comprehensive API documentation architecture for AI product platform services. Defines documentation standards for model serving APIs including versioning, deprecation policies, and performance SLA specifications. Establishes automated documentation generation integrated with ML experiment tracking and model registry systems.
Defines API documentation strategy for AI product platform services across ML engineering teams. Establishes design standards for model serving API documentation including experiment metadata and inference contract specifications. Coordinates cross-team API documentation reviews ensuring consistency across AI product integrations.
Application Security
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Learns about key OWASP Top 10 vulnerabilities and their impact on AI product security. Understands basic principles for protecting ML service API endpoints from injection and unauthorized access.
Applies OWASP recommendations for protecting AI products from typical vulnerabilities, including prompt injection and data poisoning. Conducts basic security audits of API interfaces and implements input validation for ML models.
Integrates OWASP practices into the AI product lifecycle, including protection against adversarial attacks and training data leaks. Designs multi-layer ML pipeline protection with automatic dependency scanning and anomaly monitoring.
Shapes AI product security policy based on OWASP AI Security Guidelines, implements threat modeling for ML systems. Coordinates model security reviews and ensures compliance with security standards at all development stages.
Learns secure coding principles for AI product development — input validation, escaping, secrets management. Understands risks of storing models and confidential data in open access.
Applies secure coding practices in AI products: API endpoint protection, prompt sanitization, secure credential handling. Implements rate limiting and authentication for ML services.
Designs secure AI products with data encryption, secure model storage, and audit logging. Implements automatic security checks in CI/CD and code-level adversarial attack protection.
Shapes secure coding standards for AI product teams, including ML pipeline and training data protection. Organizes regular security reviews and trains developers in defensive programming practices.
Background Jobs & Task Queues
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Uses Task Queues at a basic level in LangChain/LlamaIndex. Performs simple tasks using ready-made templates. Understands basic concepts and follows team practices.
Independently implements tasks with Task Queues in LangChain/LlamaIndex. Understands internals and optimizes performance. Writes tests using eval frameworks.
Designs task queue architectures for AI product workloads: async model inference, batch processing pipelines, and priority-based job scheduling with Celery or BullMQ. Optimizes queue throughput for user-facing latency requirements.
Defines task queue strategy for AI products: selects between Celery, SQS, and cloud-native solutions based on scale requirements. Establishes patterns for idempotent task execution, monitoring, and autoscaling workers by queue depth.
Caching
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Uses Redis at a basic level in LangChain/LlamaIndex. Performs simple tasks using ready-made templates. Understands basic concepts and follows team practices.
Independently implements tasks with Redis in LangChain/LlamaIndex. Understands internals and optimizes performance. Writes tests using eval frameworks.
Designs Redis caching architectures for AI product platforms — feature store caching for real-time inference, model prediction result caching with smart invalidation, and distributed rate limiting for model serving endpoints. Optimizes Redis memory for high-dimensional feature vector storage. Chooses between Redis, Memcached, and application-level caches for different ML serving scenarios. Mentors team on caching strategies for AI workloads.
Defines Redis caching strategy for AI product platforms — establishes standards for feature caching, inference result TTLs, and cache invalidation on model updates. Conducts design reviews of ML caching architectures and defines technical roadmap for real-time feature serving infrastructure.
CI/CD
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Learns basic CI/CD pipeline concepts in GitHub Actions for automating AI product builds and testing. Understands workflow file structure and triggers for running pipelines on ML repository changes.
Configures CI/CD pipelines in GitHub Actions for automatic testing and deployment of AI services. Integrates model quality checks, code linting, and automatic tests into the continuous integration process for product features.
Designs complex multi-stage GitHub Actions pipelines for the full AI product delivery cycle — from model training to production deployment. Implements parallel jobs, dependency caching, and conditional execution logic for ML artifacts.
Defines CI/CD strategy for the entire AI product team, standardizes reusable workflows and composite actions. Implements model quality gate checks before deployment and automatic rollback on metric degradation.
Clean Code & Refactoring
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Understands the fundamentals of Code Quality & Refactoring at a basic level. Applies simple concepts in work tasks using Python/TypeScript. Follows recommendations from senior developers when solving problems.
Independently applies code quality practices in AI product development. Refactors experiment code for reusability across model iterations. Understands trade-offs between rapid prototyping and production-ready ML code. Reviews pipeline code for reproducibility and data leakage risks.
Establishes code quality practices for AI/ML product codebases: notebook-to-production refactoring pipelines, experiment code hygiene, model serving code standards. Designs review processes balancing research velocity with production reliability. Implements quality metrics for ML pipeline code.
Defines code quality standards for AI product team: ML code review practices, experiment-to-production handoff criteria, model code documentation requirements. Conducts architectural reviews of ML pipeline designs. Establishes quality gates for model serving code.
Code Review
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Understands basic code review practices for AI products: reviewing ML pipeline code changes, checking data preprocessing logic, identifying obvious issues in model serving code. Follows team review guidelines for ML codebases.
Independently conducts code reviews for AI products: evaluates ML pipeline code quality, reviews model serving implementation patterns, checks data preprocessing correctness. Provides constructive feedback balancing research velocity with production code standards.
Designs code review processes for AI products: ML code review standards balancing research speed with production quality, model serving code review gates, data pipeline review checklists. Mentors team on reviewing ML code effectively.
Defines code review strategy for AI product team: ML code review standards, model serving code review governance, data pipeline review practices. Establishes review culture balancing research speed with production code quality.
Concurrency & Parallelism
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Understands the fundamentals of Async Programming at a basic level. Applies simple concepts in work tasks using Python/TypeScript. Follows recommendations from senior developers when solving problems.
Independently applies async programming in AI products: concurrent model inference handling, async data pipeline stages, non-blocking API endpoints for ML predictions. Understands trade-offs between sync/async in ML serving architectures.
Designs async architectures for AI products: non-blocking model inference pipelines, async data preprocessing with backpressure, concurrent model A/B testing infrastructure. Mentors team on async patterns for ML serving systems.
Defines async programming standards for AI product team: async ML pipeline architecture guidelines, concurrency patterns for model serving, non-blocking inference design reviews. Establishes best practices for async patterns in production ML systems.
Studies multithreading and parallel computing fundamentals in the context of AI product request processing. Understands concepts of threads, processes, and basic synchronization primitives for ML services.
Implements parallel request processing in AI products with proper synchronization of access to shared resources. Applies thread pools and async patterns to optimize inference service throughput.
Designs high-performance AI systems with parallel inference, request batching, and lock-free data structures. Optimizes GPU utilization through concurrent access and dynamic computational resource distribution.
Defines multithreaded AI product development standards, including concurrent access patterns and error handling strategies. Reviews critical code sections and trains the team in safe parallel programming.
Database Optimization
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Studies database indexing fundamentals and their impact on query performance in AI products. Understands index types and basic creation principles for tables with ML metrics and user data.
Creates efficient indexes for AI product tables, optimizing queries for inference logs, user data, and model metrics. Analyzes query execution plans and resolves performance issues.
Designs indexing strategy for high-load AI products considering read/write patterns, partitioning, and vector search. Optimizes indexes for analytical queries and real-time embedding search.
Defines indexing and data management standards for the AI product platform. Leads optimization of ML artifact storage and search, trains teams on effective index usage in the context of AI data.
Distributed Tracing
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Studies OpenTelemetry fundamentals for tracing and monitoring AI products — spans, traces, metrics. Understands the value of distributed tracing for diagnosing issues in ML services.
Instruments AI product services with OpenTelemetry for end-to-end tracing from user request to model inference. Configures metric and trace exporters for ML pipeline performance analysis.
Designs comprehensive AI product observability system based on OpenTelemetry with custom spans for inference stages. Implements trace correlation with business metrics and automatic anomaly detection in model behavior.
Defines AI product instrumentation standards through OpenTelemetry, including semantic conventions for ML operations. Implements observability-driven development practices and trains teams on effective tracing usage.
E2E Testing
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Studies end-to-end testing fundamentals for AI products — verifying complete user scenarios from UI to ML backend. Understands the difference between E2E and other testing types and their role in ensuring AI product quality.
Develops E2E tests for AI products covering critical user scenarios with model response correctness verification. Sets up test environments with mock models and implements automatic E2E test execution in CI.
Designs E2E testing strategy for AI products covering edge cases, A/B tests, and degradation scenarios when models are unavailable. Implements visual regression tests and automatic ML service response quality verification.
Defines E2E testing standards for the AI product portfolio, including multimodal scenario and integration testing. Fosters a quality engineering culture and ensures test coverage of the critical user path.
Git & Workflows
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Understands basic Git advanced features for AI product work: branching strategies for model experiment tracking, cherry-pick for porting ML pipeline fixes, stash for switching between feature and training branches. Follows team conventions for versioning notebooks and model artifacts.
Independently uses Git advanced features for AI products: manages experiment branches with rebase for clean history, resolves conflicts in pipeline configuration files, implements Git hooks for model validation checks. Understands trade-offs between monorepo and multi-repo strategies for ML codebases.
Designs Git workflows for AI product teams: implements monorepo strategies for shared ML components, optimizes LFS configuration for model artifact management, automates experiment branch lifecycle with CI integration. Mentors team on managing reproducible model training environments through version control.
Defines Git strategy for AI product teams: establishes standards for experiment tracking through version control, designs repository architecture for ML monorepos with shared components, drives adoption of Git-based model versioning across data science and engineering teams.
GraphQL
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Understands basics of GraphQL schema design for ML model serving and experiment tracking APIs. Writes simple queries and mutations for model metadata and prediction endpoints. Follows team conventions for exposing AI feature catalogs through GraphQL interfaces.
Independently designs GraphQL schemas for AI product APIs with efficient model serving query patterns. Implements resolvers for feature store data, experiment metadata, and prediction result aggregations. Writes comprehensive schema tests and generates interactive API documentation.
Designs GraphQL API architecture for AI product platforms spanning model serving, experiment tracking, and feature management. Defines schema evolution strategy supporting multiple model versions and A/B experiment configurations. Optimizes resolver performance for large-scale prediction result aggregation and real-time model monitoring queries.
Defines GraphQL API strategy for AI product platforms spanning model serving and MLOps tooling. Establishes schema design standards for ML data types, experiment metadata, and inference result structures. Conducts API design reviews ensuring consistency across AI product teams and coordinates federated schema governance.
Infrastructure as Code
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Learns Infrastructure as Code fundamentals through Terraform for deploying AI product infrastructure. Understands concepts of providers, resources, and state for managing ML service cloud infrastructure.
Describes AI product infrastructure in Terraform — compute resources for inference, model storage, network settings. Manages environments through modules and workspaces for various ML pipeline stages.
Designs modular Terraform infrastructure for AI products with reusable modules for GPU clusters and ML platforms. Implements automatic inference resource scaling and integrates IaC into the model deployment CI/CD process.
Defines IaC standards for the AI product platform, creates a library of Terraform modules for standard ML infrastructure patterns. Implements policy-as-code for GPU resource cost control and security compliance.
Integration Testing
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Understands basics of integration testing for ML pipeline components. Writes simple tests verifying data flow between model serving endpoints and application layers. Follows team testing guidelines for API contracts with inference services.
Independently designs integration tests for ML model serving pipelines and feature engineering workflows. Applies contract testing for inference API boundaries. Integrates model validation tests into CI/CD ensuring prediction consistency across deployments.
Designs integration testing strategy for end-to-end AI product pipelines from data ingestion to model serving. Implements automated validation across training, evaluation, and deployment stages. Optimizes test pyramid balancing model accuracy tests with infrastructure integration checks. Mentors team on testing ML system boundaries and data drift detection.
Defines integration testing strategy for AI product portfolios spanning multiple model types and serving infrastructures. Establishes quality standards for ML pipeline integration points including data quality gates and model performance thresholds. Implements shift-left testing culture with early validation of training data pipelines and feature store contracts.
Kubernetes & Orchestration
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Studies basic Kubernetes concepts for deploying AI services — pods, services, deployments. Understands the role of container orchestration in ensuring reliable ML model operation in production environments.
Deploys AI product services in Kubernetes with resource limit configuration, health checks, and autoscaling. Manages configurations through ConfigMaps and Secrets for various ML models and environments.
Designs Kubernetes infrastructure for AI products with GPU scheduling, canary model deployments, and automatic inference scaling. Implements service mesh for traffic management between ML service versions.
Defines AI product orchestration strategy in Kubernetes, standardizes Helm charts and operators for ML workloads. Implements GitOps practices for infrastructure management and ensures zero-downtime model deployment.
LLM & Generative AI
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Integrates pre-built LLM APIs into product features using standard SDKs. Writes basic prompts for user-facing functionality such as summarization or Q&A. Tests LLM outputs manually against expected behavior and documents edge cases for product scenarios.
Designs prompt chains and multi-step LLM workflows for complex product features. Implements A/B testing frameworks to compare LLM-powered feature variants and measure user engagement. Establishes evaluation criteria for AI output quality and builds feedback loops from user interactions.
Architects end-to-end LLM product systems with graceful degradation, caching strategies, and cost controls. Defines prompt engineering standards across the product organization. Leads cross-functional evaluation of AI features combining quantitative metrics with qualitative UX research and user safety analysis.
Defines LLM Applications strategy at the team/product level. Establishes standards and best practices. Conducts reviews.
Understands RAG fundamentals: document chunking, embedding generation, vector search, and context injection. Sets up basic RAG pipelines using LangChain or LlamaIndex for product Q&A features.
Builds production RAG systems with hybrid search (dense + sparse retrieval), re-ranking, and metadata filtering. Implements evaluation frameworks measuring retrieval relevance and answer faithfulness for user-facing features.
Designs enterprise RAG architecture with multi-tenant knowledge bases, access control integration, and real-time document ingestion. Implements agentic RAG with iterative retrieval and self-correcting answer generation for complex product workflows.
Defines RAG Architecture strategy at the team/product level. Establishes standards and best practices. Conducts reviews.
Understands the fundamentals of Vector Databases. Applies basic practices in daily work. Follows recommendations from the team and documentation.
Independently applies Vector Databases in practice. Understands trade-offs of different approaches. Solves typical tasks independently.
Has deep expertise in Vector Databases. Designs solutions for production systems. Optimizes and scales. Mentors the team.
Defines Vector Databases strategy at the team/product level. Establishes standards and best practices. Conducts reviews.
Logging
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Studies structured logging fundamentals and its importance for monitoring AI products in production. Understands the difference between unstructured and JSON logs, can add basic log messages to AI services.
Implements structured logging in AI products with contextual information about requests, inference time, and model results. Configures log correlation between microservices and ensures traceability of user scenarios.
Designs comprehensive logging system for AI products tracking inference latency, drift metrics, and prediction anomalies. Implements semantic log enrichment for rapid ML pipeline issue diagnosis.
Defines structured logging standards for the AI product platform, including correlation between training and inference services. Shapes log analysis practices for identifying model degradation patterns and product metric bottlenecks.
MCP & AI Tools
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Understands basic AI agent development concepts including tool-calling patterns, prompt engineering for agent behavior, and simple agent loop architectures. Follows team examples for building product features with agents that can search, summarize, and perform actions on behalf of users. Tests agent responses for quality and safety using provided evaluation datasets and human review workflows.
Independently develops AI agent features for products including multi-step task completion, context-aware decision making, and integration with external APIs and databases. Implements agent safety mechanisms — input validation, output filtering, action confirmation flows, and rate limiting for autonomous operations. Designs evaluation frameworks for agent quality measuring task completion rates, hallucination frequency, and user satisfaction across diverse product scenarios.
Designs production AI agent systems for complex product workflows with multi-agent collaboration, persistent memory, and adaptive behavior based on user interaction patterns. Implements advanced agent capabilities including self-correction through reflection, dynamic tool selection, and graceful degradation when agent confidence is low. Architects observability infrastructure for agent systems — tracing reasoning chains, monitoring tool usage costs, and measuring end-to-end task success rates in production.
Defines AI agent development strategy and quality standards for product engineering teams across the organization. Establishes agent safety frameworks, evaluation methodologies, and production deployment guidelines for user-facing agent experiences. Drives architectural decisions on agent infrastructure — choosing between hosted vs self-managed agent runtimes, defining tool ecosystem governance, and establishing cross-product agent capability sharing patterns.
Message Queues & Event Streaming
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Studies Apache Kafka fundamentals and event-driven architecture for AI products. Understands concepts of topics, producers, and consumers for asynchronous data processing in ML pipelines.
Implements AI product integration with Kafka for asynchronous event processing — collecting training data, streaming predictions. Configures producers and consumers with message delivery guarantees for ML services.
Designs event-driven AI product architecture based on Kafka with user-based partitioning, exactly-once semantics, and stream processing. Implements real-time feature store and streaming data processing for online inference.
Defines Kafka usage strategy for the AI product platform, standardizes event schemas and topic topology. Ensures scalability and fault tolerance of event-driven architecture for ML data processing.
Metrics & Monitoring
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Studies monitoring fundamentals with Prometheus and metric visualization in Grafana for AI products. Understands concepts of metrics, alerts, and dashboards for tracking ML service health in production.
Configures AI product monitoring in Prometheus with inference, latency, and resource utilization metrics. Creates informative Grafana dashboards for tracking product KPIs and ML model health in real time.
Designs comprehensive AI product monitoring system with custom metrics for tracking drift, accuracy, and business KPIs. Implements multi-level alerts with escalation and automatic model degradation diagnosis through Grafana.
Defines monitoring strategy for the AI product portfolio, standardizes metrics and dashboards across teams. Fosters a data-driven decision-making culture based on monitoring product and technical ML system metrics.
MLOps
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Understands model serving basics for AI products: REST/gRPC inference endpoints, model versioning for A/B testing, and basic latency/throughput requirements. Follows team guidelines on integrating model predictions into product features. Understands differences between batch and real-time inference.
Implements model serving for AI product features: multi-model inference pipelines, feature store integration for real-time enrichment, and A/B testing infrastructure for model comparison. Configures auto-scaling based on inference load patterns. Implements model fallback strategies for high-availability product features.
Designs model serving architecture for AI products: multi-model orchestration with routing logic, real-time feature computation for inference enrichment, and cost-optimized serving with tiered model selection. Implements serving observability: latency percentiles, prediction quality metrics, and cost-per-inference tracking. Creates model deployment governance for AI products. Mentors team on production ML patterns.
Defines model serving strategy for AI product platform. Establishes inference SLA targets, cost management frameworks, and model deployment governance. Conducts architecture reviews for AI serving infrastructure. Drives adoption of efficient model serving patterns across product teams.
Networking
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Studies network protocol fundamentals and their role in AI product operation — HTTP, TCP, DNS. Understands basic principles of network communication between client applications and ML services.
Applies network protocol knowledge to optimize communication between AI product components. Configures load balancing, SSL/TLS, and proper timeout handling for inference requests.
Designs AI product network architecture considering latency, bandwidth, and geographic user distribution. Optimizes data transfer between training and inference services in multi-regional deployments.
Defines network strategy for the AI product platform, including CDN for models, edge inference, and routing optimization. Coordinates with infrastructure teams to ensure required network interaction quality.
OOP & Design Patterns
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Understands the fundamentals of Design Patterns at a basic level. Applies simple concepts in work tasks using Python/TypeScript. Follows recommendations from senior developers when solving problems.
Independently applies design patterns for AI products: strategy pattern for interchangeable model backends, pipeline pattern for ML data processing stages, factory pattern for model instantiation. Explains pattern trade-offs for ML system extensibility in code review.
Has deep expertise in design patterns for AI systems: designs extensible ML architectures with strategy and pipeline patterns, implements plugin systems for model backends and data processors, optimizes pattern usage for ML system performance. Mentors team on architectural patterns for production AI applications.
Defines design pattern standards for AI product teams: establishes architectural review practices for ML system patterns, creates guidelines for pattern application in AI/ML codebases, conducts training on patterns for extensible ML architectures.
Prompt Engineering
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Understands prompt engineering fundamentals including temperature, system prompts, and few-shot examples. Experiments with different prompt structures to improve AI model outputs. Documents effective prompts for product features.
Designs multi-step prompt chains for complex product features. Uses structured output formats (JSON mode, function calling) to integrate AI responses into product workflows. Evaluates prompt performance with A/B testing and quality metrics.
Architects prompt systems for production AI features with fallback strategies, guardrails, and content filtering. Designs evaluation frameworks measuring prompt reliability, latency, and cost across model versions. Implements advanced techniques: constitutional AI patterns, self-consistency, and dynamic prompt generation.
Defines prompt engineering standards and governance for AI product teams. Establishes prompt review processes, quality gates, and deployment pipelines for production AI features. Creates organizational prompt registries with versioning, access control, and usage analytics. Evaluates emerging techniques (DSPy, LMQL, prompt compilers) for team adoption.
REST API
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Understands basic REST API concepts for AI products: endpoint design for model inference, request/response formats for ML predictions, API authentication basics. Follows team conventions for model serving API structure.
Independently designs REST APIs for AI products: model inference endpoint design, async prediction APIs with polling/callbacks, batch processing endpoints. Understands best practices for ML API versioning, model A/B testing endpoints, and rate limiting for inference services.
Designs API architecture for AI products: model serving API gateway patterns, inference endpoint optimization for latency/throughput, API versioning for model evolution. Defines API contracts for ML pipeline components. Mentors team on best practices for production ML API design.
Defines API strategy for AI products at product level: ML serving API standards, model versioning and lifecycle governance, inference API performance requirements. Conducts API architecture reviews for ML platform services and establishes developer experience standards.
System Design
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Learns basic distributed system design principles for AI products. Understands concepts of scalability, availability, and fault tolerance as applied to ML services and inference pipelines.
Designs AI product service architecture considering inference load, prediction caching, and horizontal scaling. Selects appropriate patterns for integrating ML models into the product architecture.
Develops architecture for complex AI product systems with real-time inference, model A/B testing, and graceful degradation. Designs fault-tolerant data processing pipelines with delivery guarantees and idempotency.
Defines architectural principles and patterns for the AI product ecosystem, including event-driven processing and CQRS for ML data. Leads design of platform solutions to accelerate new AI product development.
Type Systems
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Studies typing fundamentals and its importance for AI product code reliability. Understands the difference between static and dynamic typing, uses basic type annotations in Python and TypeScript for ML projects.
Applies strict typing in AI products to prevent errors in data processing and inference results. Uses generics, union types, and schema validation to ensure data correctness at service boundaries.
Designs type-safe AI product interfaces with runtime validation, typed model configurations, and strict API contracts. Implements code generation for types based on ML pipeline data schemas.
Defines typing standards for AI product teams, including type-safe abstractions for ML operations and data pipelines. Ensures type consistency between frontend, backend, and ML services of the product platform.
Unit Testing
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Understands basic unit testing for AI product code: pytest basics for model serving endpoints, test fixtures for ML pipeline components, mocking external model APIs. Follows team practices for testing inference code separately from model training.
Independently writes unit tests for AI product code: pytest for model serving endpoints, test fixtures with mock model responses, edge case coverage for feature preprocessing. Integrates ML pipeline tests into CI/CD with deterministic seeds and snapshot assertions.
Designs testing strategy for AI product pipelines: multi-layer test pyramid for ML serving code, contract tests for model API interfaces, property-based testing for data preprocessing. Optimizes test execution balancing coverage with CI/CD speed. Mentors team on testing ML code deterministically.
Defines testing strategy at product level for AI systems: ML test pyramid standards, model validation testing governance, data quality testing frameworks. Establishes shift-left testing culture balancing research velocity with production reliability for ML pipelines.
Web Frameworks
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Uses Node.js Frameworks at a basic level in LangChain/LlamaIndex. Performs simple tasks using ready-made templates. Understands basic concepts and follows team practices.
Independently implements tasks with Node.js Frameworks in LangChain/LlamaIndex. Understands internals and optimizes performance. Writes tests using eval frameworks.
Designs Node.js service architecture for AI product backends: LLM API gateways, streaming response handlers, and inference queue processing. Optimizes for high-concurrency AI workloads with worker threads and cluster mode.
Defines architectural decisions for Node.js Frameworks at the product level. Establishes standards. Conducts design reviews and defines technical roadmap.