Lead AI/ML Engineer

Overview:

We are looking for a hands-on, full-cycle AI/ML Engineer who will play a central role in developing a cutting-edge AI agent platform. This platform is designed to automate and optimize complex workflows by leveraging large language models (LLMs), retrieval-augmented generation (RAG), knowledge graphs, and agent orchestration frameworks.

As the AI/ML Engineer, you will be responsible for building intelligent agents from the ground up including prompt design, retrieval pipelines, fine-tuning models, and deploying them in a secure, scalable cloud environment. You’ll also implement caching strategies, handle backend integration, and prototype user interfaces for internal and client testing.

This role requires deep technical skills, autonomy, and a passion for bringing applied AI solutions into real-world use.

Key Responsibilities:

• Design and implement modular AI agents using large language models (LLMs) to automate and optimize a variety of complex workflows
• Deploy and maintain end-to-end agent/AI workflows and services in cloud environments, ensuring reliability, scalability, and low-latency performance for production use
• Build and orchestrate multi-agent systems using frameworks like LangGraph or CrewAI, supporting context-aware, multi-step reasoning and task execution
• Develop and optimize retrieval-augmented generation (RAG) pipelines using vector databases (e.g., Qdrant, Pinecone, FAISS) to power semantic search and intelligent document workflows
• Fine-tune LLMs using frameworks such as Hugging Face Transformers, LoRA/PEFT, DeepSpeed, or Accelerate to create domain-adapted models
• Integrate knowledge graphs (e.g., Neo4j, AWS Neptune) into agent pipelines for context enhancement, reasoning, and relationship modeling
• Implement cache-augmented generation strategies using semantic caching and tools like Redis or vector similarity to reduce latency and improve consistency
• Build scalable backend services using FastAPI or Flask and develop lightweight user interfaces or prototypes with tools like Streamlit, Gradio, or React
• Monitor and evaluate model and agent performance using prompt testing, feedback loops, observability tools, and safe AI practices
• Collaborate with architects, product managers, and other developers to translate problem statements into scalable, reliable, and explainable AI systems
• Stay updated on the latest in cloud platforms (AWS/GCP/Azure), software frameworks, agentic frameworks, and AI/ML technologies

Prerequisites:

• Strong Python development skills, including API development and service integration
• Experience with LLM APIs (OpenAI, Anthropic, Hugging Face), agent frameworks (LangChain, LangGraph, CrewAI), and prompt engineering
• Experience deploying AI-powered applications using Docker, cloud infrastructure (Azure preferred), and managing inference endpoints, vector DBs, and knowledge graph integrations in a live production setting
• Proven experience with RAG pipelines and vector databases (Qdrant, Pinecone, FAISS)
• Hands-on experience fine-tuning LLMs using PyTorch, Hugging Face Transformers, and optionally TensorFlow, with knowledge of LoRA, PEFT, or distributed training tools like DeepSpeed
• Familiarity with knowledge graphs and graph databases such as Neo4j or AWS Neptune, including schema design and Cypher/Gremlin querying
• Basic frontend prototyping skills using Streamlit or Gradio, and ability to work with frontend teams if needed
• Working knowledge of MLOps practices (e.g., MLflow, Weights & Biases), containerization (Docker), Git, and CI/CD workflows
• Cloud deployment experience with Azure, AWS, or GCP environments
• Understanding of caching strategies, embedding-based similarity, and response optimization through semantic caching

Preferred Qualifications:

• Bachelor’s degree in Technology (B.Tech) or Master of Computer Applications (MCA) is required; MS in similar field preferred
• 7–10 years of experience in AI/ML, with at least 2 years focused on large language models, applied NLP, or agent-based systems
• Demonstrated ability to build and ship real-world AI-powered applications or platforms, preferably involving agents or LLM-centric workflows
• Strong analytical, problem-solving, and communication skills
• Ability to work independently in a fast-moving, collaborative, and cross-functional environment
• Prior experience in startups, innovation labs, or consulting firms a plus