3. Data, Science & AI

Systematically evaluates LLM applications via prompt testing, hallucination detection, and performance metrics for quality assurance.

Memory-efficient data discovery and loading for industrial finance datasets from Parquet, Feather, and DuckDB formats.

Enables creation, editing, and analysis of spreadsheets including formulas, formatting, and data visualization for data processing and analysis tasks.

Benchmarking tool to measure CODEBASE.md's impact on AI agent efficiency by comparing parallel runs with and without the guide.

Enables debugging, tracing, and production monitoring of DSPy applications with LLM call tracking and MLflow integration.

Optimizes AI agents and models using Bayesian and statistical methods, providing a lightweight alternative to MIPROv2/GEPA for programs with rich feedback signals.

Constructs DSPy-powered ReAct agents for tool-calling and multi-step reasoning to solve complex tasks in production environments.

Assists in composing complex DSPy modules with ensemble optimization, multi-chain comparisons, and sequential pipelines for advanced LLM application development.

Refines DSPy outputs by enforcing constraints and selecting optimal results through iterative refinement or best-of-N selection methods.

Declarative kubectl-style CLI for managing Letta AI agent fleets with shared configurations, memory, and tools.

Builds scalable data pipelines, ETL/ELT processes, and cloud-optimized data infrastructure for reliable and efficient data platforms.

Provides statistical analysis, machine learning modeling, and data storytelling to deliver actionable business insights from complex datasets.

Expert in building and deploying production NLP systems using transformer models, with emphasis on multilingual support and real-time performance.

Enables persistent codebase intelligence for AI agents, allowing retention and utilization of knowledge across sessions via MCP integration.

Specializes in deploying and optimizing machine learning models for production, emphasizing scalability, real-time inference, and edge deployment.

Designs and optimizes prompts for LLMs, focusing on architecture, evaluation, and production systems to ensure reliability and efficiency.

Expert in SQL query optimization, database design, and performance tuning across PostgreSQL, MySQL, SQL Server, and Oracle.

Expert in building scalable machine learning systems from training to production deployment with emphasis on reliability and optimization.

Transforms raw data into actionable business insights using SQL, Python, and BI tools for visualization and statistical analysis.

Optimizes database performance through query tuning, index strategies, and scalability solutions across multiple database systems.

Provides advanced quantitative analysis for financial modeling, algorithmic trading, and risk management using statistical methods and mathematical modeling.

Expert in designing, deploying, and optimizing large language model systems for scalable and efficient production applications.

Provides expert data research services including data mining, statistical analysis, and pattern recognition to extract insights from complex datasets for evidence-based decisions.

Optimizes AI agent workflows through task decomposition, agent selection, and coordination to maximize team performance and resource efficiency.