infrastructure that hold it all together. You'll pair with senior engineers, ship small pieces early, and grow into owning features end to end.

This is a hands-on role. We care more about what you've built than what you've memorised.

Must have

Strong Python fundamentals clean, readable, tested code
Hands-on experience (projects, internships, or work) with LLM APIs or open-source models
Understanding of how RAG works and where it breaks chunking strategy, retrieval quality, context limits
Experience building REST APIs, ideally with FastAPI
Working knowledge of Docker building images, running containers, docker-compose
SQL proficiency; comfortable with MySQL or a similar relational database
Basic familiarity with AWS core services
Git and a collaborative development workflow (branches, pull requests, reviews)
Fast API and Relational DB needed.

Nice to have

Vector databases: pgvector, Qdrant, Chroma, FAISS, or similar
Frameworks: LangChain, LlamaIndex, or a from-scratch equivalent and knowing when not to use them
Eval tooling: Ragas, DeepEval, promptfoo, or custom harnesses
LLM observability: Langfuse, LangSmith, or OpenTelemetry-based tracing
Fine-tuning basics: LoRA/QLoRA, PEFT, dataset preparation
CI/CD (GitHub Actions), infrastructure-as-code, or Kubernetes exposure
Contributions to open-source AI projects or a public portfolio

Artificial Intelligence Engineer