Manager, Agentic Systems Engineering

Architect the Agent Stack: Lead the design and deployment of AI agents and Python for custom logic, tool-calling, and LLM integration. Drive Iterative Quality: Move beyond \"it works\" to \"it\u0027s accurate.\" You will oversee the iterative refinement of agent prompts, RAG (Retrieval-Augmented Generation) pipelines, and output validation to ensure high-fidelity outcomes. Metrics-Driven Leadership: Define and track KPIs that actually matter: Agent Accuracy Rate, RCA Lead Time Reduction, and Test Coverage Expansion via AI. Bridge the Gap: Translate high-level QA strategies into technical requirements for AI agents that can analyze customer logs, identify patterns, and suggest test improvements autonomously. Hands-on Mentorship: Stay in the code. We need a leader who can troubleshoot a Python script or an agent workflow alongside their team when the going gets tough. Academic Foundation: Bachelor\u0027s or Master\u0027s degree in Computer Science, Data Science, Software Engineering, or a related quantitative field. Proven Track Record : 5+ years in Software Engineering or Quality Engineering, with at least 2-3 years of hands-on experience building and deploying AI-enabled applications or automation frameworks. Leadership Experience: 2+ years in a formal management or lead role, specifically driving high-velocity engineering teams through the full development lifecycle. Soft Skills \u0026 Leadership Style The \"Scientist\" Mindset: A passion for experimentation. You don\u0027t get frustrated when an agent fails; you analyze the trace, adjust the prompt/data, and try again. Strategic Vision: The ability to explain to stakeholders how \"AI for QA\" translates to faster ship times and lower operational costs. Candor \u0026 Mentorship: A leader who provides direct, actionable feedback and fosters a culture of \"building in public\" and sharing AI learnings across the org.