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Artificial Intelligence / Machine Learning Engineer - RAG / LLM

Artificial Intelligence / Machine Learning Engineer - RAG / LLM

IAI solution Pvt LtdBangalore
5 days ago
Job description

Description :

Role : AI / ML Engineer (LLMs, RAG & Agent Systems)

Location : Bangalore, India (On-site)

Type : Full-time

About the Role :

As an AI / ML Engineer, youll be part of a small, fast-moving team focused on developing LLM-powered agentic systems that drive our next generation of AI products.

Youll 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.
  • (ref : hirist.tech)

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