GenAI & Machine Learning

Upskilling Program

Build an observability and eval stack for LLM systems with tracing, dashboards, and CI-integrated evals.

Run a client discovery engagement that turns a vague ask into a signed, estimated project scope.

Reverse-engineer a legacy system with CDC and entity resolution to build a clean data sync pipeline.

Evaluate CoT, self-consistency, and tree-of-thought prompting against a zero-shot baseline for cost vs quality.

Build a multi-turn support assistant with a role-contract system prompt, then red-team it against 8 attack classes.

Build a provider-agnostic LLM client with async streaming and caching to compare cost and latency across providers.

Build a fault-tolerant async pipeline that ingests 50,000 paginated records from a flaky API with retries.

Build a production RAG pipeline over policy documents with chunking, embeddings, and vector retrieval.

Improve retrieval using hybrid BM25 and vector search, cross-encoder reranking, and RAGAS evaluation.

Build a multi-hop RAG agent that decomposes questions, retrieves evidence across hops, and cites its answers.

Fine-tune a small open-source model with LoRA/QLoRA to beat a larger general-purpose model on a narrow task.

Build a tool-calling agent on MCP-standardized servers, gated by an arbiter layer that evaluates its actions.

Build a stateful claims agent in LangGraph with persistent memory and a human-in-the-loop approval step.

Turn a notebook prototype into a production async API service, containerized and shipped with CI/CD.

Build an observability and eval stack for LLM systems with tracing, dashboards, and CI-integrated evals.

Run a client discovery engagement that turns a vague ask into a signed, estimated project scope.

Reverse-engineer a legacy system with CDC and entity resolution to build a clean data sync pipeline.

Evaluate CoT, self-consistency, and tree-of-thought prompting against a zero-shot baseline for cost vs quality.

Build a multi-turn support assistant with a role-contract system prompt, then red-team it against 8 attack classes.

Build a provider-agnostic LLM client with async streaming and caching to compare cost and latency across providers.

Build a fault-tolerant async pipeline that ingests 50,000 paginated records from a flaky API with retries.

Build a production RAG pipeline over policy documents with chunking, embeddings, and vector retrieval.

Improve retrieval using hybrid BM25 and vector search, cross-encoder reranking, and RAGAS evaluation.

Build a multi-hop RAG agent that decomposes questions, retrieves evidence across hops, and cites its answers.

Fine-tune a small open-source model with LoRA/QLoRA to beat a larger general-purpose model on a narrow task.

Build a tool-calling agent on MCP-standardized servers, gated by an arbiter layer that evaluates its actions.

Build a stateful claims agent in LangGraph with persistent memory and a human-in-the-loop approval step.

Turn a notebook prototype into a production async API service, containerized and shipped with CI/CD.

Build an observability and eval stack for LLM systems with tracing, dashboards, and CI-integrated evals.

Run a client discovery engagement that turns a vague ask into a signed, estimated project scope.

Reverse-engineer a legacy system with CDC and entity resolution to build a clean data sync pipeline.