3. Data, Science & AI

Combines vector and keyword search to improve retrieval accuracy in RAG systems and search engines.

Optimizes Apache Spark job performance through partitioning, caching, shuffle tuning, and memory configuration for efficient data processing pipelines.

Enables implementation of data quality validation using Great Expectations, dbt tests, and data contracts for robust data pipelines.

Optimizes LLM performance, reliability, and output quality using production-ready prompt engineering patterns.

Optimizes SQL queries and database schemas using indexing and EXPLAIN analysis to enhance performance and resolve slow queries.

Transforms data into compelling narratives using visualization and context for stakeholder presentations and executive reports.

Enables comprehensive evaluation of LLM applications through automated metrics, human feedback, and benchmarking to measure AI performance and quality.

Guides optimal KPI dashboard design with metric selection, visualization best practices, and real-time monitoring patterns.

Optimizes embedding models and chunking strategies for semantic search and RAG applications in AI systems.

Enables efficient semantic search and nearest neighbor queries through vector database optimization for AI-driven retrieval systems.

Calculates portfolio risk metrics including VaR, CVaR, Sharpe ratio, Sortino ratio, and drawdown for risk assessment.

Analyzes meeting transcripts and recordings using AI to identify communication patterns and provide actionable feedback for leadership development.

Debugs LangChain and LangGraph agents by fetching and analyzing execution traces from LangSmith Studio for error investigation and performance insights.

Analyzes completed PRs with agent involvement to extract behavioral lessons, identify patterns, and generate actionable recommendations for improving agent skills and documentation.

Assists developers in building AI features using the Vercel AI SDK, including agents, chatbots, RAG systems, tool calling, and structured output.

Creates and manages example implementations for AI SDK functions to validate provider integrations, demonstrate capabilities, and serve as test fixtures.

Enables development of machine learning and AI applications in Rust, supporting model training, inference, and deep learning with Rust libraries.

Provides techniques for evaluating and refining AI agent outputs through self-critique, rubric-based systems, and iterative improvement pipelines.

Command-line interface for interacting with Model Context Protocol (MCP) servers, enabling access to AI tools, APIs, and data sources via terminal commands.

Provides AI-powered code review and plan analysis using Gemini 3 Pro for large context processing (over 200k tokens).

Creates comprehensive handoff documents for seamless AI agent session transfers, preventing context exhaustion and enabling uninterrupted continuation.

Provides optimal strategies for integrating and utilizing HeyGen's AI avatar video creation API effectively.

Orchestrates multiple AI agents with specialized expertise to perform parallel tasks or provide comprehensive analysis from diverse perspectives.

Designs optimized database schemas for SQL and NoSQL with normalization, indexing, and performance strategies to ensure data integrity and efficiency.