Product Manager Agentic AI

• Working technical fluency in agentic AI system design: agent orchestration and planning (e.g., ReAct-style reasoning, task decomposition, multi-agent coordination), tool/function calling, and agent memory patterns.
• Solid working knowledge of the surrounding AI stack: LLM APIs and fine-tuning/prompting tradeoffs, RAG pipelines, vector databases, knowledge graphs, and evaluation/observability tooling for LLM-based systems.
• Familiarity with common agent frameworks and protocols (e.g., LangGraph, AutoGen, CrewAI-style orchestration, or equivalent in-house frameworks) and emerging standards such as MCP (Model Context Protocol) for tool/agent interoperability.
• Track record of writing clear PRDs/specs for ML-driven features, defining success metrics for probabilistic (not just deterministic) systems, and running structured experimentation.
• Comfort partnering closely with engineering and applied AI/ML teams on architecture-level tradeoffs - you don't need to write production code, but you need to hold your own in a systems design conversation.
• Strong stakeholder management and communication skills - able to translate deep technical tradeoffs into business language for leadership, sales, and customers, and vice versa.
• Prior experience in enterprise B2B SaaS, security/intelligence, fintech, or another data-sensitive domain is a plus, given Innefu Labs' customer base.
• Bachelor's degree in Computer Science, Engineering, or a related technical field; an MBA or equivalent is a plus but not a substitute for hands-on technical depth.

Requirements What You Will Own

• Define and own the product vision, strategy, and roadmap for an agentic AI product built from zero - problem discovery, architecture direction, MVP scoping, and scale-up.
• Translate ambiguous, open-ended problems into a phased agentic system design: which tasks are agent-driven vs. deterministic, where human-in-the-loop checkpoints belong, and how autonomy expands release over release.
• Partner with engineering on core architecture decisions - agent orchestration and planning layers, tool-calling/function-calling design, memory and state management, RAG and retrieval pipelines, and multi-agent coordination patterns.
• Own the evaluation framework for agent quality - task success rate, hallucination/error rate, latency, cost-per-task, and safety guardrails - and use it to drive prioritization, not just measure it after the fact.
• Define prompt, tool, and knowledge-source specifications working directly with applied AI/ML engineers; review agent behavior transcripts and failure cases as part of the regular product cycle.
• Run structured discovery with enterprise customers and internal stakeholders to identify high-value workflows for autonomous or semi-autonomous automation.
• Own the build-vs-buy and framework decisions in collaboration with engineering (agent frameworks, vector databases, LLM providers, orchestration layers) and stay current on the fast-moving agentic AI ecosystem.
• Define and track north-star and input metrics for the product - adoption, task completion, time-saved, cost-to-serve - and report outcomes to leadership.
• Own the end-to-end roadmap prioritization, sprint-level scoping with engineering, and release planning; write clear PRDs, user stories, and acceptance criteria for agent-based features.

• Represent the product externally - customer demos, RFP/technical proposal support, and partner/analyst conversations - with the technical depth to answer architecture-level questions directly.