We're building LLM-powered products, and we need a junior engineer who can take an idea from a notebook to a production API. You'll work across the full applied-AI stack: retrieval pipelines, prompt design, open-source model serving, evaluation, and the backend services and cloud 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 team-oriented 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 (Pune)
🏢 InfoVision
📍 Pune