AI Engineer

Overview

Senior hands-on build role for Nexperia's agent engineering practice in Bangalore, owning the architecture of AI agents and the user-facing surfaces (chat UI, IDE extensions, CLI tools, APIs) through which people use them.

What you'll do

• 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.
• Own the build of user-facing surfaces for Copilot-style products: chat UI, IDE extensions and CLI tools.
• Build the backend APIs connecting 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 frontend and backend components.
• 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 CI/CD, monitoring, versioning/rollback and observability.
• Own the governance bar for citizen-built agents (security, data access, cost and quality) in partnership with security/compliance and the Integration Architect.
• Publish and maintain standards (prompting, evals, architecture patterns) that spoke teams build against.
• Own the design of the self-service component catalog spokes build on, and the support model for citizen developers using it.

What you'll need

• 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.

Nice to have

• 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).
• Strong 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 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.
• Experience or interest in developer enablement or platform engineering: documentation, SDKs, or internal tooling built for non-specialist users.

Details

• Location: Bangalore, India.
• Works with the AI and Data Centers of Enablement, the Integration Architect, platform engineering, security and compliance, with visibility up to the CIO and IT Management Team.
• Mentors two Junior AI Engineers.