Benefits

Onsite
Competitive salary
Opportunity for advancement

Overview

Senior Product Manager, AI Agents on our Agentic AI / AI Engineering Team will provide strategic product leadership for enterprise-grade AI Agents built on our Agentic Platform, leveraging AWS Bedrock, SageMaker Unified Studio, and leading Agentic AI frameworks. This role is central to designing and operationalizing AI Agents capable of complex decision-making and multi-step task execution across the enterprise — transforming how we operate and deliver business value. You\'ll lead cross-functional initiatives that define, scale, and continuously evolve our next-generation Agentic AI ecosystem. This position is ideal for a seasoned product leader who thrives at the intersection of strategy, innovation, and execution — and who is passionate about building the enterprise foundations for autonomous, explainable, and responsible AI systems.

Responsibilities

Define and execute the comprehensive product strategy for enterprise AI agents, aligning with company-wide AI initiatives and business goals.
Lead the end-to-end product lifecycle — ideation, design, experimentation, delivery, and scaling — of AI agent features and capabilities.
Develop single and multi-agent strategies for deployment via AWS Bedrock AgentCore, SageMaker Unified Studio, and frameworks such as LangChain and LangGraph.
Establish roadmaps and prioritization frameworks for multi-modal AI experiences supporting RAG, tool integration, human-in-the-loop capabilities, and cross-agent collaboration.
Identify and validate high-impact AI agent use cases across business units.
Drive adoption and scaling of AI agent solutions across the enterprise.
Coordinate go-to-market plans, launch communications, and performance tracking for internal and external rollouts.
Partner with engineering, data science, security, and compliance to translate business needs into resilient, scalable, and compliant agentic solutions.
Collaborate with the Agentic AI Data Platform Team to ensure reliable, governed, and scalable data access for agent workflows.
Define repeatable agent patterns, including data flows for contextual reasoning, real-time ingestion, and autonomous decision-making.
Implement data governance and auditability frameworks ensuring compliance with privacy-by-design and industry standards.
Establish metrics and KPIs to measure agent performance, impact, and business ROI.
Partner across Product, Data Science, Security, Legal, and Operations to deliver end-to-end AI capabilities.
Manage relationships with technology partners and vendors in the AI ecosystem.
Lead A/B testing, agent evaluation, and post-deployment analysis to drive continuous improvement.
Contribute to responsible AI practices, emphasizing transparency, explainability, and ethical data use.

Qualifications

Education

Bachelor\'s degree (or equivalent experience) in Computer Science, Engineering, Business, or Information Systems.
Advanced degree preferred.

Experience

5+ years in Product Management, with at least 2+ years focused on AI, ML, or enterprise software.
Experience building or managing LLM-based applications or AI infrastructure.
Proven success leading large-scale, cross-functional technology initiatives.
Familiarity with AWS technologies (S3, VPC, SageMaker, Bedrock) and modern data/AI stack architectures.

Skills & Competencies

Strong background in strategy formulation, prioritization, and stakeholder alignment.
Expertise in managing complex software product requirements and MVP definition.
Proficiency in establishing and tracking KPIs, business value metrics, and AI performance indicators.
Excellent communication and leadership skills to engage senior executives and technical teams.
Demonstrated ability to balance innovation with reliability in mission-critical systems.
Experience working under uncertainty and making informed product decisions with limited data.
Commitment to Responsible AI, governance, and enterprise transparency.

Preferred Attributes

Experience in agentic frameworks, multi-agent orchestration, or autonomous systems.
Deep understanding of data lineage, model evaluation, and MLOps practices.
Ability to translate complex technical capabilities into clear business value propositions.
Track record of researching emerging AI trends and integrating relevant advancements into product strategy.

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