AI Engineer

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.

This is a hands-on engineering role. It requires real experience building AI agents with LangChain and LangGraph, RAG with vector databases, and solid full stack fundamentals in Python, Java and JavaScript. This is an AI-native engineering environment: AI coding agents such as Claude Code and Cursor are part of the daily workflow. The role requires 2+ years of relevant experience.

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.

Backend Engineering

• 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

• Build JavaScript/React interfaces for agent features, including chat, streaming responses and result views.

• 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.

• Vector Databases: pgvector, Qdrant, Pinecone or ChromaDB.

• Evaluation & Tracing: LangSmith or equivalent agent evaluation tooling.

• Backend: Python, FastAPI, Java and Spring Boot.

• Frontend: JavaScript, React and TypeScript.

• 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

Essential: AI Agents

• 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.

Essential: Engineering

• 2+ years of software engineering experience building production applications.

• Strong coding ability in Python, with working knowledge of Java and JavaScript.

• 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.

Desirable

• 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.

• IoT, energy, industrial or real-time data experience.