AI Engineering & Agentic AI Architect

The layered model of AI systems, choosing the right abstraction, transformers, tokens, context windows and embeddings.

What an LLM really does, prompt vs context engineering, and structured outputs with validation outside the model.

Production RAG: ingestion, chunking, hybrid search, reranking, tenant-safe retrieval, data modeling and picking retrieval by data type.

Safe tool pipelines, what an agent is, the loop as a state machine, and the workflow-vs-agent decision guide.

Chaining, routing, parallelization, evaluator–optimizer, planning, verification and long-running tasks.

MCP as an interoperability layer, capability discovery and versioning, and when frameworks help or hurt.

Short-term state, long-term memory with provenance, and when multiple agents are genuinely justified.

Choosing and routing models by evaluation, sandboxing coding agents, and designing a coding-agent platform.

Golden datasets, LLM judges, metrics that matter, testing strategy and red-teaming whole systems.

Prompt injection, OWASP risks as controls, secrets and authorization, and approval design.

Observability, reliability, cost, latency, API design, CI/CD for AI and the readiness checklist.

Reference architecture, the model gateway, serverless agents and multi-tenant AI SaaS.

A repeatable design method applied to an enterprise assistant, a support agent, a coding-agent platform and an agent platform.

Five build labs with deliverables and acceptance criteria, from a validated LLM service to a production agent on AWS.

The senior interview framework, staff-level thinking, a portfolio that signals senior, a six-month plan and the mastery checklist.