AI Engineer

1. Agentic AI

- Built and deployed AI agents (beyond chatbots), including agents that call tools/APIs and execute multi-step tasks

- Hands-on experience with agent frameworks: LangGraph, LangChain, Semantic Kernel, AutoGen or CrewAI

- Experience with tool/function calling, MCP (Model Context Protocol), and multi-agent workflows

- Implemented human-in-the-loop patterns, where the agent pauses for human approval before performing risky actions

- Worked with agent state and memory (e.g. Redis, Postgres), and added safety guardrails

2. Generative AI

- 2.5–3+ years of hands-on GenAI experience (specifically GenAI, not only general AI/ML)

- Experience with LLM APIs: Azure OpenAI / OpenAI

- Hands-on with RAG, embeddings, and vector databases (Azure AI Search, pgvector, Pinecone, FAISS)

- Prompt engineering and structured outputs

3. Engineering and production

- Strong Python, FastAPI, Docker, Kubernetes/AKS, and CI/CD

- Testing and evaluation of AI outputs (golden test sets, Ragas, DeepEval, LLM-as-judge)

- Monitoring and observability (OpenTelemetry, LangSmith, Langfuse, App Insights/Dynatrace)

- Managed cost, reliability, and PII/security handling

4. Platform mindset

- Developed reusable components, templates, SDKs, or frameworks adopted by multiple teams