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

at IAI solution

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

IAI solution

Company

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

at IAI solution

Posted by Anajli Kanojiya

1 - 2 yrs

₹5L - ₹6L / yr

Bengaluru (Bangalore)

Skills

Large Language Models (LLM)

LangChain

MongoDB

Docker

Job Title: 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.

Job Title: 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.

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About IAI solution

Founded :

2025

Type :

Product

Size :

20-100

Stage :

Raised funding

About

N/A

Company social profiles

N/A

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