Overview
Lead AI Engineer focused on building production-grade agentic AI and applying Agentic Engineering across the software development lifecycle (SDLC).
What you'll do
- Drive adoption of Agentic Engineering practices across the SDLC using AI agents.
- Build AI-assisted workflows for requirements analysis, code generation, understanding, refactoring, testing, debugging, documentation, code review, and deployment.
- Design agent workflows for large codebase understanding, context management, tool use, multi-step execution, and failure recovery.
- Establish best practices for context management, tool calling, agent orchestration, guardrails, human-in-the-loop, and autonomous task execution.
- Design and implement evaluations (Evals) for agent correctness, reliability, code quality, task completion, regression, and effectiveness.
- Architect production multi-agent and agentic systems for planning, reasoning, memory/context, execution, validation, and error recovery.
What you'll need
- 6+ years of software engineering / AI engineering experience.
- Strong software engineering fundamentals with experience building production-grade applications and services.
- Demonstrable experience building production-grade agentic AI systems beyond simple chatbots or basic RAG.
- Experience with Agentic Evaluation / Agent Evals, including evaluation frameworks, datasets, test scenarios, metrics, regression tests, and quality gates.
- Hands-on experience with Python and modern backend/API development.
- Experience with LLMs/GenAI, agent orchestration, tool calling, and RAG.
- Experience with agent frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Google ADK, or equivalent.
Details
- Candidate should have practical exposure to using AI agents as engineering tools within the SDLC.
Read the full description and apply on the company’s own careers page.
