AI Engineering
Ship reliable LLM systems: RAG, agents, evals, fine-tuning, and inference ops.
Playbook: Launch a RAG feature end to end
Take retrieval-augmented answers from empty repo to evaluated production feature.
RAG Pipelines
Choose chunk sizes, overlaps, and structure-aware splits so retrieved context actually helps the model answer.
Combine BM25-style lexical search with dense embeddings and fuse results so both rare terms and paraphrases are found.
Score groundedness, relevance, and completeness with judge prompts plus deterministic checks wired into CI.
AI Agents
Shape tool names, parameters, descriptions, and error messages so the agent calls the right tool with valid arguments.
Bound agent autonomy with step budgets, spend caps, human gates, and checkpointing so failures stay cheap and debuggable.
Split work across specialized agents with typed handoff contracts so context survives delegation without ballooning.
Prompt Engineering
Organize identity, rules, tools, and output contracts into sections that evolve without breaking behavior.
Choose and order examples covering edge cases so the model generalizes instead of copying surface patterns.
Choose and order examples covering edge cases so the model generalizes instead of copying surface patterns.
Get machine-parseable JSON reliably using schema-first prompting, constrained decoding, and repair loops.
Fine-tuning & Adaptation
Clean, deduplicate, and balance instruction-response pairs so fine-tuning learns behavior rather than noise.
Pick the cheapest adaptation layer that solves the problem using a decision ladder.
Inference & MLOps
Allocate milliseconds across retrieval, prompting, generation, and streaming so p95 meets product targets.
Route by task complexity, cache aggressively, and enforce budgets so unit economics hold as usage grows.
Treat all retrieved content as untrusted input: isolate instructions from data, gate actions, and fuzz continuously.
MCP Server Building
Tool schema design, transport choice, error contracts, and test harness for Model Context Protocol servers.
Journey Playbooks
Take retrieval-augmented answers from empty repo to evaluated production feature.
Ship an autonomous agent whose failure modes are cheap, visible, and reversible.
Reduce monthly inference spend measurably while keeping answer quality within tolerance.
Traced, permission-governed agent infrastructure auditors and engineers both trust.
A scaffolded, schema-tight MCP server agents integrate without hand-holding.