1–6 yrsBengaluruOn-siteDirect career pageEst. 18–35 LPA
Posted 14h ago
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Job description
Structured overview of role & requirements
About This Role
- Own the architecture and production delivery of agent systems, including reasoning, planning, tool use, memory, APIs, chat interfaces, IDE extensions and CLI tools.
- Set release-quality standards with MLOps/evaluation partners; oversee production deployment, monitoring, rollback and failure handling.
- Establish safety and architecture standards for citizen-built agents, maintain a reusable component catalog, and review and mentor two Junior AI Engineers.
Minimum Requirements
- Work Experience Required: 1–6 years of hands-on experience building LLM or agent systems.
- Strong Python and/or TypeScript; hands-on experience with agent frameworks/SDKs such as Claude Agent SDK/MCP, LangGraph or AutoGen, or custom orchestration; must have shipped an agentic system to production.
- Solid grasp of traditional ML/AI, including methods such as classification, regression, clustering or feature engineering.
- Experience designing REST or GraphQL APIs and streaming/WebSocket APIs, with familiarity with LLM streaming and token-by-token rendering.
Ideal Candidate Profile
- A hands-on senior engineer who can own agent architecture and end-to-end products, from orchestration and backend services through user-facing developer tools and deployment.
- Has operated production AI systems and can work across agent quality, CI/CD, observability, versioning and rollback; manufacturing or semiconductor experience is a plus.
- Can turn AI platform patterns into governed, reusable tools and standards for other teams, while providing technical review and mentorship.