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 prototyping AI product features and generating boilerplate ML pipeline code. Applies AI assistants to explore model architectures and draft experiment configurations. Follows team guidelines for evaluating AI-generated code quality and prompt engineering for product development tasks.
Independently applies ChatGPT/Claude for AI product development — generating training pipelines, creating evaluation scripts, and drafting model documentation. Understands trade-offs between AI-assisted and manual code generation for ML system components. Designs effective prompt chains for complex product engineering workflows.
Defines ChatGPT/Claude adoption strategy for AI product engineering teams. Establishes standards for LLM-augmented development workflows, prompt engineering practices, and AI tool evaluation criteria. Conducts reviews of AI-assisted development patterns ensuring responsible and effective usage across the product organization.
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.
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.
Defines Cursor IDE strategy at the team/product level. Establishes standards and best practices. Conducts reviews.
Understands basic GitHub Copilot usage for AI product development: leveraging suggestions for ML pipeline boilerplate, using Copilot Chat for model serving code questions, accepting completions for data processing functions. Follows team guidelines for validating AI-generated code in ML contexts.
Independently configures GitHub Copilot for AI product workflows: crafts effective prompts for ML pipeline code, uses Copilot Chat for debugging model serving issues, evaluates suggestion accuracy for data processing and feature engineering. Understands trade-offs between Copilot speed and ML code correctness requirements.
Defines GitHub Copilot strategy for AI product teams: establishes guidelines for AI-assisted ML code development, designs validation workflows for AI-generated model and pipeline code, drives responsible adoption of Copilot across data science and engineering teams.
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.
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.
Defines organizational algorithmic strategy for AI/ML: enterprise model training optimization standards, cross-team ML algorithm evaluation frameworks, computational resource governance. Makes strategic decisions on ML infrastructure investments based on algorithmic efficiency analysis.
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.
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.
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.
Defines organizational API documentation strategy for AI product platforms spanning model serving, MLOps, and data science tooling. Designs platform-level documentation architecture for unified ML API references and experiment reproducibility specifications. Establishes enterprise API governance standards for AI services across all product teams.
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.
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.
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.
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.
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.
Defines organizational Redis and caching strategy for AI platforms — evaluates Redis vs specialized feature stores, establishes enterprise caching governance for ML workloads, and designs reference architectures for real-time feature serving at organizational scale.
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.
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.
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.
Defines organizational code quality strategy for AI/ML development: ML code lifecycle standards, research-to-production quality pipelines, cross-team model code reuse patterns. Makes decisions on ML tooling investments and establishes organization-wide experiment code quality bar.
Cloud Providers
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Understands basic AWS concepts for AI/ML workloads — S3 for data storage, SageMaker for model training, and Lambda for inference endpoints. Uses ready-made CloudFormation templates for ML pipeline infrastructure. Performs simple operations like monitoring SageMaker training jobs under senior guidance.
Independently configures AWS services for ML pipelines — SageMaker training jobs, Step Functions orchestration, and API Gateway for model endpoints. Writes Terraform/CDK for standard ML infrastructure patterns. Understands VPC networking for secure model serving and IAM policies for SageMaker role management.
Defines AWS infrastructure strategy for AI product portfolios spanning training, serving, and MLOps workloads. Establishes IaC standards for SageMaker deployments and ML pipeline automation. Conducts architecture reviews optimizing ML workload cost-performance and drives FinOps for GPU compute spending.
Defines organizational cloud strategy for AI/ML workloads evaluating multi-cloud vs AWS-centric architectures. Designs enterprise-grade ML infrastructure with multi-region SageMaker deployments and cross-account model governance. Establishes FinOps practices for GPU compute optimization and ML training cost management at organizational scale.
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.
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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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.
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.
Containerization
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Understands basic Docker concepts for AI products: containerized model inference services, Docker images for ML pipeline dependencies, basic docker-compose for local model serving stack. Uses ready-made configurations for GPU-enabled containers.
Independently configures Docker for AI products: multi-stage builds for model serving images, GPU-enabled container orchestration, docker-compose for local ML pipeline development. Understands container networking for microservice-based ML architectures and security basics for model containers.
Defines Docker infrastructure strategy for AI products: GPU container platform standards, ML model container lifecycle governance, container registry and versioning policies. Conducts architecture reviews for containerized ML serving and optimizes FinOps for GPU workloads.
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.
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.
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.
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.
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.
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.
Defines organizational GraphQL API strategy for enterprise AI platforms spanning model serving, MLOps, and data science tooling. Designs platform-level schema architecture for unified access to ML capabilities across business units. Establishes enterprise API governance for AI services balancing standardization with domain-specific model serving requirements.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Understands the fundamentals of OOP & SOLID Principles at a basic level. Applies simple concepts in work tasks using Python/TypeScript. Follows recommendations from senior developers when solving problems.
Independently applies OOP/SOLID in AI product codebases: clean abstraction layers between model serving and business logic, interface segregation for ML pipeline components, dependency inversion for swappable model backends. Understands trade-offs between OOP patterns and functional approaches in ML code.
Defines OOP/SOLID standards for AI product team: ML pipeline abstraction layer guidelines, model serving interface contracts, experiment code architecture patterns. Conducts reviews of class hierarchy decisions balancing research flexibility with production maintainability.
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.
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.
Defines organizational AI strategy including prompt engineering as a core competency. Drives adoption of prompt engineering across engineering, product, and business teams. Establishes enterprise-level AI governance: model selection criteria, cost management frameworks, and responsible AI practices. Influences industry standards through publications and community engagement.
Relational Databases
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Understands basic PostgreSQL for AI product work: storing model metadata and experiment results, basic queries for feature retrieval, pgvector extension basics for embedding storage. Follows team conventions for ML data schemas and connection management.
Independently designs schemas for AI product data: optimizes queries for feature retrieval and model metadata, implements pgvector indexes for embedding similarity search, configures connection pooling for ML serving workloads. Understands trade-offs between PostgreSQL and specialized vector databases for AI applications.
Defines PostgreSQL data strategy for AI products: establishes standards for ML data storage and pgvector usage, designs database architecture for feature stores and model registries, drives adoption of PostgreSQL best practices for AI/ML data infrastructure.
Defines organizational data strategy for AI/ML platforms: evaluates PostgreSQL vs specialized databases for enterprise ML infrastructure, designs multi-region data architectures for global AI products, establishes governance for ML data storage and vector database technology decisions.
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.
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.
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.
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.
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.