AI GenAI Software Engineer Junior Associate

• Build, optimize, and maintain end-to-end RAG pipelines (document ingestion, chunking, embeddings, vector indexing, retrieval, and reranking).
  
• Integrate LLM APIs (Anthropic Claude, OpenAI, Gemini, open-source models) into scalable backend services using FastAPI or Python.
  
• Use AI-native coding setups (Claude Code CLI, Cursor, GitHub Copilot) to explore codebases, write unit tests, and accelerate deployment cycles.
  
• Design structured outputs, function/tool calling, and agent workflows using frameworks like LangChain, LlamaIndex, or native SDKs.
  
• Implement basic evals, guardrails, and logging to reduce hallucinations, measure retrieval quality, and monitor API costs.
  

Benefits

Core Requirements

• Programming: Solid foundation in Python (async, REST APIs, clean object-oriented code) and Git.
  
• GenAI & RAG: Hands-on experience with vector databases (e.g., Chroma, Qdrant, Pinecone, or pgvector) and embedding models.
  
• AI Tooling Native: Daily, active user of CLI or IDE agentic coding tools (Claude Code, Cursor, or similar)—you know how to guide AI agents and rigorously inspect generated code.
  
• Proof of Work: At least 1 shipped or working project beyond basic chat (e.g., semantic search tool, custom RAG on documents, automated agent, or GitHub repo).
  

Good to Have

• Familiarity with Docker, Linux environment, and basic cloud deployment (AWS/GCP).
  
• Experience with Model Context Protocol (MCP) or multi-agent orchestration frameworks.
  
• Exposure to front-end integration (Streamlit, Next.js, or React basics).