Product Manager Agentic AI

Innefu Labs; Delhi; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Innefu Labs seeks a Product Manager who has taken an agentic AI product from whiteboard to production. You will own vision, architecture tradeoffs, and go-to-market for a multi-agent, LLM-driven product line, collaborating with engineering, applied AI, and design to ship autonomous systems with minimal human input.

You will participate in architecture discussions, define roadmaps, and create PRDs. This is a builder's role requiring strong technical fluency and stakeholder management in an

Qualifications

• 8–12 years in product management owning AI/ML products from concept to live release.
• Fluency in agent orchestration, planning, and multi-agent coordination.
• Knowledge of LLM APIs, RAG pipelines, and evaluation tooling.
• Enterprise B2B SaaS experience; data-sensitive domains a plus.
• Bachelor's in CS/Engineering; MBA or equivalent is a plus but not required.

Responsibilities

• Define and own the product vision, strategy, and roadmap for an agentic AI product built from zero.
• Translate problems into phased agentic system design with clear autonomy boundaries.
• Partner with engineering on core architecture decisions—agent orchestration and memory.
• Own the evaluation framework for agent quality and use it to drive prioritization.
• Define prompt, tool, and knowledge-source specs with applied AI/ML engineers.
• Run structured discovery with enterprise customers and internal stakeholders.
• Own build-vs-buy decisions and stay current on agent AI ecosystem.
• Define north-star metrics and report outcomes to leadership.
• Own end-to-end roadmap prioritization, sprint scoping, and PRDs.
• Represent the product externally via demos and technical proposals.

Skills

AI product management

Agentic AI systems design

Architecture discussions

Roadmap prioritization

Stakeholder management

Cross-functional collaboration

Education

Bachelor's degree in Computer Science or related field

MBA or equivalent (plus)

Tools

LangGraph

AutoGen

CrewAI

Neo4j

Qdrant

pgvector

Job description

Innefu Labs is looking for a Product Manager who has personally taken an agentic AI product from a blank whiteboard to a production system — not someone who has only added AI features to an existing roadmap. You will own the vision, architecture-level tradeoffs, and go-to-market for a multi-agent, LLM-driven product line, working shoulder-to-shoulder with engineering, applied AI, and design to ship autonomous systems that reason, plan, and act with minimal human handholding.

This is a builder's role. You are expected to be technically fluent enough to sit inside architecture discussions on agent orchestration, retrieval pipelines, and evaluation frameworks — and translate that into a roadmap, prioritization model, and business outcome the rest of the company can rally behind.

5 Days Work from office

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.

What You Bring

• 8–12 years in product management, with demonstrable ownership of at least one AI/ML or agentic AI product taken from concept/zero to a live, adopted release — not just a feature add-on to an existing product.
• 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.

Nice to Have

• Direct experience owning or contributing to a knowledge-graph or retrieval-heavy product (e.g., Neo4j, Qdrant/pgvector or similar vector stores).
• Exposure to agent safety/guardrail design — RBAC for agent actions, audit logging, human-in-the-loop escalation design.
• Past experience as an engineer, applied scientist, or technical founder before moving into product.