AI/ML Engineer

About The Role

LLM-powered agentic systems

next-generation AI products

retrieval-augmented generation (RAG)

multi-agent coordination

tool-using AI architectures

Key Responsibilities

• Design and implement components for LLM-based systems (retrievers, planners, memory, evaluators).
• Build and maintain RAG pipelines using vector databases and embedding models.
• Experiment with reasoning frameworks like ReAct, Tree of Thought, and Reflexion.
• Collaborate with backend and infra teams to deploy and optimize agentic applications.
• Research and evaluate open-source LLM frameworks to identify best-fit architectures.
• Contribute to internal tools for evaluation, benchmarking, and scaling AI agents.
  

Required Skills

• Strong foundation in ML/DL theory and implementation (PyTorch preferred).
• Understanding of transformer architectures, embeddings, and LLM mechanics.
• Hands-on experience with prompt engineering, tool calling, and structured output design.
• Proficiency in Python, Git/GitHub, and data processing pipelines.
• Familiarity with RAG systems, vector databases, and API-based model inference.
• Ability to write clean, modular, and reproducible code.
  

Preferred Skills

• Experience with LangChain, LangGraph, Autogen, or CrewAI.
• Hands-on with the Hugging Face ecosystem (transformers, datasets, etc.).
• Working knowledge of Redis, PostgreSQL, MongoDB.
• Experience with Docker, deployment workflows, and MLOps (CI/CD, model versioning, cloud).
• Familiarity with OpenAI, Anthropic, vLLM, or Ollama APIs.
  

What We Value

• Deep understanding of fundamentals over tool familiarity.
• A researcher’s mindset with an engineer’s execution ability.
• Collaborative, growth-oriented approach to problem-solving.
  

Preferred Background

IITs, IIITs, NITs

research institutions

research or applied AI/ML background