Lead AI Engineer - Agentic Engineering

About this role

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Skills you'll use

What you'll do

• Agentic Engineering & AI-Augmented SDLC
• Drive the adoption of  Agentic Engineering practices across the software development lifecycle , using AI agents to augment and automate engineering workflows.
• Leverage tools and approaches such as  Claude Code, Claude Code Skills, PI, Hermes Agent , and comparable AI coding/engineering agents as part of day-to-day software development.
• Build AI-assisted workflows covering  requirements analysis, code generation, code understanding, refactoring, testing, debugging, documentation, code review, and deployment .
• Design agent workflows capable of understanding large codebases, managing context, using tools, executing multi-step engineering tasks, and recovering from failures.
• Establish best practices around  context management, context engineering, tool calling, agent orchestration, guardrails, human-in-the-loop workflows, and autonomous task execution .
• Design and implement  Evals  to measure agent correctness, reliability, code quality, task completion, regression, and overall effectiveness.
• Continuously evaluate emerging agentic coding tools and techniques and identify opportunities to improve engineering productivity and software quality.
• Production-Grade Agentic AI
• Architect and develop  multi-agent and agentic systems  capable of performing complex, multi-step tasks in production environments.

Who can apply

• 6+ years of software engineering / AI engineering experience , with strong hands-on development 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 applications.
• Strong understanding of  Agentic Evaluation / Agent Evals , including designing evaluation frameworks for autonomous and multi-agent systems.
• Experience creating  evaluation datasets, test scenarios, metrics, automated regression tests, and quality gates  for agentic applications.
• Ability to evaluate agents beyond final-answer accuracy, including  planning, tool use, reasoning trajectory, context handling, reliability, safety, latency, cost, and task completion .
• Strong 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 .
• Strong understanding of  multi-agent architectures, planning, reasoning, context management, tool use, memory, and agent execution .

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