3. Data, Science & AI

Designs and implements agent-based models to simulate complex systems through individual agent interactions, capturing emergent behaviors in social, biological, and economic contexts.

Comprehensive guide for integrating LLMs into game design, implementation, and testing workflows using AI-driven prompting techniques.

Designs and implements Monte Carlo simulations for uncertainty quantification, risk analysis, and probabilistic modeling in scientific and financial applications.

Adapts large language models to specific tasks using techniques like LoRA, PEFT, and instruction tuning for domain-specific performance.

Assists in implementing data governance frameworks, including data catalogs, lineage, quality rules, and compliance management.

Performs orbital mechanics computations including trajectory propagation, maneuver planning, and perturbation analysis for spacecraft.

Provides AI-driven protein structure prediction, structural analysis, and quality assessment using AlphaFold2 and ColabFold, with experimental data integration.

A tool for aerospace engineers to design missions, compute launch windows, optimize trajectories, and analyze payload/propellant constraints.

Provides comprehensive safety guardrails for LLM applications including content moderation, jailbreak prevention, and PII detection for secure AI deployment.

Specializes in building MCP servers, client integrations, and resource providers for Anthropic's Claude ecosystem, enabling seamless LLM tool development.

Implements Retrieval-Augmented Generation (RAG) systems with optimized chunking, embeddings, and vector store integration for semantic search and document QA.

Expert in designing and optimizing production-ready LLM applications including RAG systems, prompt engineering, and multi-agent architectures.

Enables reliable, structured output generation from LLMs using JSON mode, function calling, and Pydantic validation for seamless system integration.

Assists in designing and orchestrating role-based multi-agent systems using CrewAI for complex collaborative AI workflows.

Provides design patterns and analysis methods for feedback control systems, including PID tuning, MPC, and stability analysis for robotics and automation applications.

Enables distributed training of large AI models across multiple GPUs/nodes using DDP, FSDP, and DeepSpeed for memory efficiency and throughput optimization.

Designs event tracking, attribution modeling, and funnel analysis architecture for data-driven user behavior insights and experimentation.

Enables natural, low-latency voice interactions using speech-to-speech and pipeline architectures with real-time AI processing.

Analyzes climate data and projections using CMIP6 for impact assessment, downscaling, and scenario modeling in climate science.

Enables systematic quantitative trading research through backtesting, alpha generation, and factor model development for statistical arbitrage strategies.

Creates playable games via natural language prompts to AI, covering prompting strategies, frameworks, and rapid prototyping for game development.

Optimizes ML models for edge deployment via quantization, pruning, and TensorRT, reducing size and improving inference speed.

Enables structured communication between LLM agents using message passing, shared memory, and event-driven patterns for coordinated multi-agent systems.

Specializes in causal inference, counterfactual reasoning, and effect estimation using structural causal models and tools like dowhy.