AIML Engineer

Job Overview

We are seeking a talented AI/ML Engineer to design, build, and deploy next-generation Agentic AI systems. In this role, you will move beyond simple prompt engineering to build autonomous, multi-agent workflows, stateful applications, and cognitive architectures. You will leverage LangChain and LangGraph to develop resilient AI agents capable of reasoning, planning, and executing complex tasks to solve real-world business problems.

Key Responsibilities

• Agentic Architecture: Design and implement autonomous AI agents and multi-agent systems using LangGraph for stateful, cyclic orchestration.
• LLM Application Development: Build production-grade LLM applications, chains, and custom tools using LangChain.
• Advanced RAG: Develop and optimize Advanced Retrieval-Augmented Generation (RAG) pipelines using vector databases and hybrid search techniques.
• Memory & State Management: Implement complex memory systems (short-term, long-term, and conversational) to maintain context in multi-turn agent interactions.
• Evaluation & Guardrails: Establish rigorous evaluation frameworks (using tools like TruLens, Ragas, or LangSmith) and safety guardrails for agent behavior.
• Model Fine-Tuning & Selection: Evaluate, select, and fine-tune open-source and proprietary LLMs (e.g., OpenAI, Anthropic, Llama) for specific agent tasks.
• API Integration: Connect AI agents securely to internal databases, external APIs, and enterprise software systems.
• Production Deployment: Deploy, monitor, and scale agentic workflows in cloud environments (AWS/GCP/Azure) using LLMOps best practices.

Required Technical Skills

• Core Languages: Mastery of Python and standard ML libraries (NumPy, Pandas, Scikit-learn).
• Frameworks: Deep hands-on experience with LangChain and LangGraph (essential).
• Generative AI: Strong understanding of LLM architectures, prompt engineering, and agent patterns (ReAct, Plan-and-Solve, Reflection).
• Vector Databases: Experience with vector stores like Pinecone, Chroma, Milvus, or Qdrant.
• Software Engineering: Proficient with Git, Docker, and building RESTful APIs (FastAPI/Flask).
• Cloud & DevOps: Familiarity with cloud platforms and basic CI/CD pipelines.