Artificial Intelligence Engineer

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