AI Engineer

Ecolibrium; Bengaluru Urban; On-site; INR 2,600,000 - 5,200,000; Full time

Job summary

Ecolibrium is seeking a skilled MTS-II in Bengaluru, India to build AI agents and full-stack features for our energy and IoT platform. You will design agent workflows, RAG pipelines, and tool integrations that operate in production, while developing backend services and clean interfaces for users.

You will work with Python (FastAPI) or Java (Spring Boot), and collaborate with product, data science, and senior engineers to deliver reliable, scalable agent capabilities and frontend integrations.

Qualifications

• Hands-on experience building AI agents or agentic workflows with LangChain and LangGraph, shipped to real users.
• Practical experience building RAG pipelines with a vector database.
• Understanding of tool calling, prompt and context design, and how agents fail.
• Exposure to evaluating LLM or agent outputs where results are not deterministic.
• 2+ years of software engineering experience building production applications.
• Experience building REST APIs with FastAPI or Spring Boot.
• Good fundamentals in data structures, databases and testing.

Responsibilities

• Build AI agents and agentic workflows that run in production, using LangChain and LangGraph.
• Design and implement RAG pipelines with document ingestion, embeddings, and retrieval.
• Develop tool-calling agents and MCP server integrations for safe data access.
• Write tests and ensure code quality with unit and integration testing.
• Develop backend services in Python (FastAPI) or Java (Spring Boot) and expose APIs.
• Collaborate with product, data science, and engineering to ship features to production.
• Use CI/CD pipelines (Docker) and maintain logging and tracing for agent runs.

Skills

LangChain

LangGraph

RAG pipelines

Tool calls

REST APIs

Data structures

Databases

Testing

Tools

FastAPI

Spring Boot

React

LangSmith

Claude Code

Cursor

Job description

We are looking for a Member of Technical Staff II (MTS-II) to build AI agents and the full stack features around them for our energy and IoT platform. AI agents are the core of this role: the MTS-II will build agentic workflows, RAG pipelines and tool integrations that run in production, along with the backend services and interfaces that deliver them to users.

THE WORK

Key Responsibilities

AI Agent Engineering (Core Focus)

• Build AI agents and agentic workflows using LangChain and LangGraph that run in production, not just as demos.
• Design and implement RAG pipelines, including document ingestion, chunking, embeddings, retrieval and re-ranking over vector databases.
• Build tool-calling agents and MCP servers that let agents act on platform data and APIs safely.
• Implement agent evaluation: test datasets, quality scoring and regression checks so changes to prompts, models or tools can be measured.
• Handle the practical realities of LLMs in production, including latency, cost per request, token limits, rate limits, fallbacks and guardrails.
• Iterate on prompts, context design and agent graphs based on evaluation results and real usage.
• Build backend services and APIs in Python (FastAPI) or Java (Spring Boot) that expose and orchestrate agent capabilities.
• Integrate agents with existing platform services, databases and event streams.
• Write clean, tested code with unit and integration tests as part of every change.
• Debug issues across agent logic, LLM responses, retrieval quality and backend services.

Frontend Engineering

• Integrate frontend and backend cleanly through well-defined API contracts.

Delivery & Collaboration

• Ship features to production through CI/CD pipelines using Docker, and support them after release.
• Add logging and tracing for agent runs (LangSmith or equivalent) so behaviour can be inspected and improved.
• Work closely with product, senior engineers and data science to turn requirements into working agent features.
• Use Claude Code, Cursor and equivalent AI coding agents in daily work, and review AI-assisted changes carefully.

The Environment

Technology Environment

• AI Frameworks: LangChain, LangGraph and Model Context Protocol (MCP).
• LLMs: Claude, OpenAI and open-source models via LLM APIs.
• Evaluation & Tracing: LangSmith or equivalent agent evaluation tooling.
• Backend: Python, FastAPI, Java and Spring Boot.
• Data: PostgreSQL and Redis.
• Infrastructure: AWS and Docker.
• CI/CD: GitHub Actions or equivalent modern CI/CD tooling.
• AI Tooling: Claude Code and Cursor.

WHAT WE'RE LOOKING FOR

Qualifications & Experience

• Hands-on experience building AI agents or agentic workflows with LangChain and LangGraph, ideally shipped to real users.
• Practical experience building RAG pipelines with a vector database.
• Understanding of tool calling, prompt and context design, and how agents fail.
• Exposure to evaluating LLM or agent outputs where results are not deterministic.
• 2+ years of software engineering experience building production applications.
• Experience building REST APIs with FastAPI or Spring Boot.
• Good fundamentals in data structures, databases and testing.

Essential: Ways of Working

• Curiosity and speed in learning new AI tools, models and techniques as the field moves.
• Ability to take a defined problem and deliver working software with guidance on design.
• Clear communication with engineers and product.
• Fluent use of Claude Code, Cursor or equivalent AI development tools in daily engineering work.
• Built an MCP server or MCP-based integration.
• Experience with agent evaluation frameworks or LangSmith.
• React frontend experience, including streaming UIs.
• Fine-tuning or working with open-source and small language models.
• Public projects, GitHub work or write-ups on AI agents.