AI Agent & LLM Developer

Comfortable Python programming is the main prerequisite. You should be able to write functions, work with dictionaries and lists, use pip/virtual environments, and call a REST API. Prior ML knowledge is helpful but not strictly required — the roadmap builds what you need as you go.

LangChain is a broad framework for building LLM applications — chains, RAG pipelines, and single agents. CrewAI is purpose-built for multi-agent collaboration, using a role-based model where agents with defined personas work as a team. Most production systems use both: LangChain for low-level plumbing and CrewAI (or AutoGen) for orchestrating multiple agents.

RAG is the go-to approach whenever your knowledge base is large, frequently updated, or proprietary, because it is cheap, fast to iterate, and keeps data outside the model. Fine-tuning is preferred when you need a very specific output style, tone, or structured format that prompting alone cannot achieve consistently. For most enterprise document-QA use cases, well-implemented RAG outperforms fine-tuning at a fraction of the cost.

Production systems require: (1) observability — trace every LLM call with inputs, outputs, latency, and cost; (2) guardrails — input validation, output moderation, and fallback behavior; (3) reliability — retries, timeouts, and circuit breakers around flaky LLM APIs; (4) cost control — caching repeated calls, choosing the right model tier per task; (5) evaluation — automated test suites that catch regressions when you update prompts or models.