What You'll Learn, Module by Module.

How real Data & AI systems are architected today — ML pipelines vs. agentic systems, the LLM-first enterprise stack, cloud AI platform landscape (Bedrock, Vertex AI, Azure AI Foundry), and a personal transition map from your current role into the production AI engineer career path.

Production-grade services in FastAPI — authentication, microservice patterns, Docker, Kubernetes, testing, and CI/CD pipelines that ship daily. Plus the modern AI dev toolchain: Cursor, Claude Code, OpenAI Codex, and Devin for AI-accelerated engineering workflows.

Production data engineering from the ground up: SQL at scale on PostgreSQL, document modeling in MongoDB, ETL pipeline design and orchestration with Apache Airflow and Prefect, large-scale data processing with Databricks, BI dashboards, and time-series forecasting on live market data.

Prompt engineering as a production discipline, OpenAI and Claude SDKs at depth, multi-modal AI applications, and the full vector database landscape — Pinecone, ChromaDB, Weaviate, Qdrant, and FAISS — with RAG pipelines engineered for correctness, latency, and retrieval quality.

Design and build production multi-agent systems using the complete modern stack: LangGraph and LangChain for orchestration, LlamaIndex for retrieval-augmented agents, CrewAI and AutoGen for multi-agent collaboration, PydanticAI for type-safe agents, Semantic Kernel for enterprise integration, and OpenAI Agents SDK and Claude Agent SDK for provider-native agents — with tool calling, agent memory, MCP, and A2A communication.

Ship AI you can trust: RAG and retrieval quality evaluation with RAGAS, end-to-end monitoring with TruLens, automated LLM and agent testing with DeepEval, production tracing with LangSmith, LLM evaluation benchmarks, agent monitoring dashboards, and guardrails for safety, alignment, and compliance in production AI systems.

Take agentic systems to enterprise production: AWS Bedrock Agents for serverless AI orchestration, Azure AI Foundry for enterprise-grade deployment and management, Google Vertex AI for GCP-native agent hosting, Kubernetes for container orchestration, MLOps fundamentals for model lifecycle management, and cost and latency optimization for reliable AI services.

Design, build, evaluate, and deploy a production agentic AI system on a live product — text-to-SQL, text-to-dashboard, or multi-agent deep research — and present it at Demo Day. A portfolio-ready, production-evaluated system you engineered end to end.