Job Description: AI/ML Engineer (LLMs, RAG & Agent Systems)
Location: Bangalore, India (On-site)
Type: Full-time
About The Role
As an AI/ML Engineer, you'll be part of a small, fast-moving team focused on developing LLM-powered agentic systems that drive our next generation of AI products.
You'll work on designing, implementing, and optimizing pipelines involving retrieval-augmented generation (RAG), multi-agent coordination, and tool-using AI systems.
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 experiment with 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.
- Practical exposure to prompt engineering, tool calling, and structured output design.
- Experience 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 Hugging Face ecosystem (transformers, datasets, etc.).
- Working knowledge of Redis, PostgreSQL, or MongoDB.
- Experience with Docker and deployment workflows.
- Familiarity with OpenAI, Anthropic, vLLM, or Ollama inference APIs.
- Exposure to MLOps concepts like CI/CD, model versioning, or cloud (AWS/GCP/Azure).
What We Value
- Deep understanding of core principles over surface-level familiarity with tools.
- Ability to think like a researcher and execute like an engineer.
- Collaborative mindset, building together, learning together.
Skills:- Large Language Models (LLM) tuning, Retrieval Augmented Generation (RAG) and AI Agents
