Job Title: AI/ML Engineer
Location: Local/Hybrid
Employment Type: Full-Time
Department: Machine Learning Team
Reports To: Head of AI/CTO
About the RoleWe’re looking for a multi-disciplinary AI Engineer to design, implement, and deploy LLM-driven agents with strong backend and front-end integration. You’ll be leading efforts across LLM agent design, prompting, fine-tuning, and MLOps, while building real-world, production-grade applications with modern web technologies.
The ideal candidate combines a strong foundation in Python and AI with practical experience in agent frameworks like LangGraph, PydanticAI, and Google ADK, as well as FastAPI and front-end development.
Key ResponsibilitiesLLM Agents & Prompt Engineering- Architect and implement LLM agents using frameworks like LangGraph, PydanticAI, and Google ADK.
- Build composable, tool-augmented reasoning chains (e.g., RAG, CoT, ReAct, planner-executor).
- Integrate vector databases (e.g., FAISS, Pinecone, pgvector) and knowledge graphs (Neo4j) to support retrieval-augmented generation (RAG) and long-term chatbot memory.
- Design and maintain high-quality prompt strategies for robustness and reliability.
FastAPI, Model Context Protocol (MCP) & Backend- Develop and maintain scalable APIs using FastAPI, supporting synchronous and asynchronous agent execution.
- Integrate Model Context Protocol (MCP) to enable secure and structured access to external data and tools within agent workflows.
- Implement state tracking, context-aware input dispatch, and modular plugin integration within the control plane.
Evaluation, Testing & Observability- Build unit and behavioral tests for agents, tools, and workflows.
- Develop tooling for trace analysis, agent state debugging, and hallucination tracking.
- Compare and benchmark agent orchestration frameworks for trade-offs in speed, reliability, and usability.
Model Fine-Tuning & MLOps- Fine-tune models using LoRA, QLoRA, or full fine-tuning pipelines.
- Integrate, deploy, and monitor models in production using cloud providers.
- Set up agent logging, observability dashboards, and recovery workflows.
Front-end & User Experience- Collaborate with front-end developers or build user-facing components using React, TypeScript, or Next.js.
- Ensure seamless user and agent interaction via UI and API bridges.
Required Skills & ExperienceCore Skills:- 3+ years experience with Python in ML/AI systems and PyTorch or Tensorflow
- 1+ years experience with LLM agent development, prompt engineering, and frameworks like LangGraph, PydanticAI, and Google ADK.
- Experience with fine-tuning LLMs.
- Familiarity using vector stores like ChromaDB, Weaviate, or pgvector.
- Production experience with FastAPI, Docker, and MLOps
- Expert in Agentic Coding IDEs (Windsurf, Cursor or Claude Code)
- Bachelor’s or Master's degree in computer science
Front-end & UX:- Familiar with React, TypeScript, Next.js, or similar frameworks
- Understanding of front-end and back-end integration for AI tools
- Ability to build basic dashboards or agent interfaces
Bonus Experience- Open-source contributions to LLM/agent tooling
- Knowledge of async programming, websockets, and streaming APIs
Benefits- Competitive salary and equity
- Hybrid, flexible hours
