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 collaborative development workflow (branches, pull requests, reviews) Rapid 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 (Maharashtra)
🏢 InfoVision
📍 Maharashtra