Agent architecture: workflow vs. agent harness, tool calling, MCP, memory and context management, guardrails against prompt injection and data exfiltration.
At least one agent framework ( Bedrock AgentCore, LangGraph, OpenAI Agents SDK, or equivalent).
Grounding: RAG with vector stores and retrieval over structured data via SQL.
LLM foundations: model selection, tokenisation and cost, context window and caching, when to fine-tune or self-host.
Production operations for AI systems: observability and tracing, cost/latency optimisation, statistical regression testing in CI/CD, prompt/model versioning, infrastructure-as-code.
Software engineering fundamentals: full-stack application design, data modelling, testing strategy, secure-by-design practice (dependency scanning, secrets hygiene).
AWS in production: Lambda, ECS/Fargate, API Gateway, Bedrock, IAM.
Clear written and verbal explanation of technical trade-offs and uncertainty to non-specialists.