3. Data, Science & AI

Delivers advanced test reporting, predictive analytics, and quality dashboards for data-driven quality engineering decisions.

Validates database schema, integrity, migrations, and query performance to ensure reliable data persistence and transaction handling.

Designs autonomous testing systems using AI agents to scale quality engineering and apply PACT principles.

Enables creation of AI voice assistants, phone agents, and IVR systems for voice-based interactions and call automation.

Executes 1C:Enterprise database queries via HTTP, enabling data retrieval, analysis, and validation of 1C-specific query syntax.

Guides creation of Model Context Protocol (MCP) servers using Node/TypeScript to enable LLMs to interact with external APIs through well-defined tools.

Automates updating the Monarch Initiative publications page by fetching and processing latest data from Google Scholar, including citation metrics and new papers.

Analyzes continuous glucose monitoring (CGM) data from Nightscout to provide real-time glucose levels, trends, and diabetes management insights including A1C estimates and time-in-range statistics.

Extracts readable transcripts from JSONL session files generated by Claude Code and Codex CLI.

Generates and optimizes Salesforce SOQL queries from natural language, ensuring performance and compliance with governor limits.

Creates AI agents via Agent Script syntax, defining topics and actions for deployment with AiAuthoringBundle and GenAiPlannerBundle (v65+).

Expert in Salesforce data management, handling SOQL queries, test data creation, bulk operations, and data import/export via Salesforce CLI.

AI agent testing framework that executes tests, analyzes topic/action coverage, generates specs, and automatically fixes failing agents via 100-point scoring.

Optimizes SQL queries and indexing across PostgreSQL, MySQL, and H2 databases using EXPLAIN analysis for performance and compatibility.

Optimizes and designs effective prompts for LLMs using chain-of-thought patterns to enhance AI model performance and integration.

Performs data processing, analysis, and visualization using Python and JavaScript, including pandas operations and chart generation for insights extraction.

Designs and orchestrates multi-agent AI systems with knowledge harvesting, agent collaboration, and learning loops for autonomous agent projects.

Enables integration with vector databases for RAG, embeddings, and semantic search to build AI-powered knowledge bases.

Provides backtesting for Pine Script strategies, generating performance metrics, equity curves, win rates, and drawdown analysis for trading strategy evaluation.

Breaks down trading concepts into implementable Pine Script components for systematic strategy development and chart visualization.

Comprehensive PostgreSQL database engineering expertise covering indexing, query optimization, performance tuning, replication, and high availability for production environments.

Comprehensive guide to Apache Kafka stream processing including producers, consumers, Kafka Streams, connectors, schema registry, and production deployment.

Comprehensive guide for implementing data transformations with dbt, covering models, tests, documentation, and production workflows.

Provides a comprehensive guide for managing vector databases (Pinecone, Weaviate, Chroma) to implement semantic search, RAG systems, and similarity-based applications.