Job Summary

About the team: Digital Enablement is Nexperias fast-track enabler of AI & Digital use-cases and platform enablement. We operate as one global practice across Nijmegen, Hamburg, Kuala Lumpur, Seremban and Bangalore, covering digital strategy, cloud and infrastructure, digital factory and enterprise, data, AI and integration.

Bangalore is where we build and ship the agentic systems that put Nexperias AI patterns in front of real users: copilots, IDE tooling and internal automation that engineers across the company touch every day.

About the role

This is the senior build role for Nexperias agent engineering practice in Bangalore. You own the architecture of the agents themselves - reasoning, planning, tool-calling and memory - and you own the surfaces people actually use them through: chat UI, IDE extensions, CLI tools and the APIs behind them.

You work closely with the Integration Architect on how agents connect into IDEs and internal systems, and with the MLOps/eval function on the quality bar an agent has to clear before it ships.

This is a hands-on role. You are still writing and reviewing code, still debugging orchestration failures when something breaks in production, and still accountable for what ships - not a role that manages from a distance.

What you will do

- Architect and orchestrate agents: Design agent architecture: reasoning loops, planning, tool-calling and memory/context management.
- Build multi-step orchestration: task decomposition, tool selection and error recovery.
- Partner with the Integration Architect on connecting agents to IDEs and internal systems.
- Ship end-to-end agent products: Own the build of user-facing surfaces for Copilot-style products: chat UI, IDE extensions and CLI tools.




- Build the backend APIs that connect those surfaces to agent and LLM services, including streaming responses, session state and context handling.
- Ship integrations into developer tools (VS Code extension API, LSPs, browser extensions) and own deployment of both the frontend and backend components you build.
- Set the bar and take releases live: Review code and agent designs from the two Junior AI Engineers, and mentor them toward independent ownership.
- Define the agent quality bar with the MLOps/eval function before anything ships, and work with that function on the CI/CD, monitoring, versioning/rollback and observability an agent needs once its live.
- Enable the Spokes: Own the governance bar for citizen-built agents - what safe to ship means for security, data access, cost and quality - in partnership with security/compliance and the Integration Architect.
- Publish and maintain the standards - prompting, evals, architecture patterns - that spoke teams build against.
- Own the design of the self-service component catalog spokes build on top of, and the support model - office hours, an embedded rotation, or a ticket queue - for citizen developers using it.

What you bring

Core requirements

- 1-6 years hands-on building LLM or agent systems.
- Strong Python and/or TypeScript; comfortable owning a service end to end.
- Hands-on with agent frameworks or SDKs (Claude Agent SDK/MCP, LangGraph, AutoGen) or custom orchestration.




- Has shipped an agentic system to production, not just a prototype.
- Solid grasp of traditional machine learning/AI (e.g., classification, regression, clustering, feature engineering) in addition to LLM/agent-based approaches..
- Comfortable designing REST, GraphQL and streaming/WebSocket APIs, and familiar with LLM streaming APIs (SSE/WebSockets) and token-by-token rendering.
- Familiarity with enterprise AI/copilot platforms and cloud-native model ecosystems - Microsoft Copilot and Copilot Studio, Azure AI Foundry (model catalog, deployments, and orchestration), and AWS model configuration (Bedrock model access, inference profiles/settings) is a plus.

Valuable extras

- Robust in React/TypeScript for frontend work, and Node.js or Python for backend work.
- Experience with CI/CD tooling (GitHub Actions, Jenkins or similar), cloud infrastructure (AWS, Azure or GCP) and containers (Docker, Kubernetes).
- Observability tooling - Datadog, Prometheus/Grafana, or LLM-specific tools like LangSmith or Arize - and familiarity with LLMOps practices: prompt versioning, eval pipelines, cost tracking.
- Working knowledge of auth and session management, and awareness of security and compliance considerations for AI systems.
- Experience in the manufacturing industry, preferably semiconductor, is a plus.
- Experience or interest in developer enablement or platform engineering - documentation, SDKs, or internal tooling built for non-specialist users, not just for your own team.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Senior AI Engineer (Bengaluru)
🏢 Nexperia
📍 Bengaluru