Job
Description
As an Engineering Manager for Agentic AI at our company, you will play a crucial role in leading a small team of engineers dedicated to building an enterprise-grade Agentic AI platform and applications for recruitment. Your responsibilities will encompass owning the end-to-end delivery of Agentic AI features, being hands-on in coding, designing agentic systems, leveraging LLMs smartly, shipping production ML/LLM workflows, focusing on MLOps & Observability, ensuring EVALs & quality, handling DevOps, overseeing security & compliance, providing people leadership, and managing stakeholders effectively. **Key Responsibilities:** - Own delivery end-to-end for Agentic AI features including backlog, execution, quality, and timelines. - Be hands-on (30-50% coding) to set the technical bar in Python/TypeScript, review PRs, and solve tricky problems. - Design agentic systems focusing on tool use orchestration, planning/looping, memory, safety rails, and cost/perf optimization. - Leverage LLMs smartly for RAG, structured output, function/tool calling, multi-model routing, and evaluate build vs. buy decisions. - Ship production ML/LLM workflows including data pipelines, feature stores, vector indexes, retrievers, and model registries. - Implement MLOps & Observability practices to automate training/inference CI/CD, monitor quality, drift, toxicity, latency, cost, and usage. - Define task level metrics for EVALs & quality, set up offline/online EVALs, and establish guardrails. - Handle DevOps responsibilities including owning pragmatic infra with the team, utilizing GitHub Actions, containers, IaC, and basic K8s. - Ensure security & compliance by enforcing data privacy, tenancy isolation, PII handling, and partnering with Security for audits. - Provide people leadership by recruiting, coaching, and growing a high trust team, and establishing rituals like standups, planning, and postmortems. - Manage stakeholders effectively by partnering with Product/Design/Recruitment SMEs and translating business goals into roadmaps. **Qualifications Required (Must-Haves):** - 10+ years in software/ML with 4+ years leading engineers in high-velocity product teams. - Experience building and operating LLM powered or ML products at scale. - Strong coding skills in Python, Java, TypeScript/Node, and solid system design and API fundamentals. - Exposure to frontend technologies like React, Angular, Flutter. - Practical MLOps experience and familiarity with LLM tooling, observability & EVALs, DevOps, and AI SDLC tools. - Product mindset with a focus on measuring outcomes and making data-driven decisions. **Nice to Have:** - Experience in HRTech/recruitment domain, retrieval quality tuning, prompt engineering at scale, and knowledge of multi-agent frameworks. This role provides an exciting opportunity to lead a dynamic team in developing cutting-edge Agentic AI solutions for recruitment. As an Engineering Manager for Agentic AI at our company, you will play a crucial role in leading a small team of engineers dedicated to building an enterprise-grade Agentic AI platform and applications for recruitment. Your responsibilities will encompass owning the end-to-end delivery of Agentic AI features, being hands-on in coding, designing agentic systems, leveraging LLMs smartly, shipping production ML/LLM workflows, focusing on MLOps & Observability, ensuring EVALs & quality, handling DevOps, overseeing security & compliance, providing people leadership, and managing stakeholders effectively. **Key Responsibilities:** - Own delivery end-to-end for Agentic AI features including backlog, execution, quality, and timelines. - Be hands-on (30-50% coding) to set the technical bar in Python/TypeScript, review PRs, and solve tricky problems. - Design agentic systems focusing on tool use orchestration, planning/looping, memory, safety rails, and cost/perf optimization. - Leverage LLMs smartly for RAG, structured output, function/tool calling, multi-model routing, and evaluate build vs. buy decisions. - Ship production ML/LLM workflows including data pipelines, feature stores, vector indexes, retrievers, and model registries. - Implement MLOps & Observability practices to automate training/inference CI/CD, monitor quality, drift, toxicity, latency, cost, and usage. - Define task level metrics for EVALs & quality, set up offline/online EVALs, and establish guardrails. - Handle DevOps responsibilities including owning pragmatic infra with the team, utilizing GitHub Actions, containers, IaC, and basic K8s. - Ensure security & compliance by enforcing data privacy, tenancy isolation, PII handling, and partnering with Security for audits. - Provide people leadership by recruiting, coaching, and growing a high trust team, and establishing rituals like standups, planning, and postmortems. - Manage stakeholders effectively by partnering with Product/Design/Recruitment SMEs and translating business goals into roadmaps. **Qualifications Required (Must-Haves):** - 10+
