Strong proficiency in Python , with hands-on experience building production-grade software (not just notebooks/prototypes).
Practical experience with agentic frameworks such as LangChain, LangGraph , or similar.
Solid understanding of LLM fundamentals — prompting, tool/function calling, RAG, embeddings, context management, memory systems.
Experience designing modular, platform-agnostic architectures (i.e., not tightly coupled to a single LLM vendor or cloud provider).
Understanding of SDLC processes (CI/CD, version control, code review, testing) and how AI agents can augment or automate them.
Experience with API design , microservices, and building extensible/reusable frameworks or SDKs.
Familiarity with agent evaluation and observability tooling (e.g., LangSmith, tracing frameworks, custom eval harnesses).
Strong grasp of governance concepts for AI systems — guardrails, access control, auditability, human oversight.
