Job description

Job Description You enjoy shaping the future of product innovation as a product leader, driving value for customers, guiding successful launches, and exceeding expectations. Join our team to shape the future of enterprise agentic platform engineering by delivering a high-quality AI agent builder platform that engineers across the firm depend on every day. As a Technical Product Manager on the AI Agents Platform, you help build the foundational developer platform that engineering teams use to design, build, test, register, deploy, monitor, and operate AI agents safely and at enterprise scale. This platform provides the reusable components, runtime, and governance that enable teams to build and scale agentic experiences with speed, quality, and consistency. In this role you own the platform and governance services that carry an Agent Development Life Cycle (ADLC): Agent Cloud; Managed Agents Sandbox; the Agent Control Tower services such as kill switch, throttling, and rollback; the Gateway and Proxy; Identity and on-behalf-of (OBO) delegation; and governance as a service, including the Agent Manifest, and the Agent and MCP System of Record. You are accountable for turning lifecycle operation and governance into platform capabilities that engineering teams consume rather than rebuild, so responsible AI is the default path and operational ownership stops being an adoption gate. Job responsibilities • Develop a product strategy and vision for the agent platform and governance services so lifecycle operation, identity, and control become platform capabilities rather than wheels each engineering team reinvents. • Own, maintain, and develop a product backlog across ADLC, Agent Cloud, Managed Agents, the Control Tower, Gateway, Identity, and governance that supports the strategic roadmap and value proposition. • Own the Agent Control Tower as the firmwide control plane, where every production agent carries a canonical identity, a lifecycle state, a policy status, and an operational status, and authorized operators can disable or kill, throttle, redirect, roll back, and revoke credentials. • Deliver identity and delegation capabilities including multi-hop OBO downscoping, short-lived scoped credentials, and MCP and agent certification lifecycle. • Own governance as a service, including the Agent Manifest and release graph, and the Agent and MCP System of Record, with automatic evidence capture targeting the vast majority of governance evidence at build time. • Document the ADLC consistently across the SDK and other surfaces, and simplify Managed Agent onboarding during feasibility so governance steps enable adopters. • Embed controls for model usage, tool invocation, data access, entitlements, logging, auditability, and human oversight directly into the developer and operator workflow so compliance is the path of least resistance. • Build the framework and track key success metrics such as production conversion, operational overhead, governance automation rate, reliability, and control effectiveness. • Own end-to-end delivery and execution, planning and prioritizing the backlog of supporting scrum teams. • Partner with Legal, Compliance, Risk, Cybersecurity, Model Risk, and Data Governance to operationalize responsible AI and agentic AI requirements as product capabilities rather than manual gates. Required qualifications, capabilities, and skills • 5+ years of experience or equivalent expertise in AI product management or a relevant domain area. • Advanced knowledge of the Agent development life cycle and Spec Driven Development, design, and data analytics. • Proven ability to lead product life cycle activities including discovery, ideation, strategic development, requirements definition, and value management. • Demonstrated experience with, or a working understanding of, AI and agentic products or platforms, including foundational concepts such as LLMs and Harness. • Hands-on experience using LLMs and AI-powered tools to drive personal and team productivity, with a strong instinct for where AI can be applied to solve problems. • Familiarity with evaluating and instrumenting AI systems, including strong analytical skills to measure quality and performance for non-deterministic, agent-driven experiences. • Proven track record of delivery and new product feature creation through agile product development and the software delivery life cycle. • Strong stakeholder management and interpersonal skills, with strategic thinking, thought leadership, and the ability to influence and align cross-functional partners. • A bias for action and comfort operating in ambiguity, with the initiative to move quickly in a dynamic, fast-evolving environment. • Excellent written and oral communication skills. Preferred qualifications, capabilities, and skills • Experience delivering platform, infrastructure, or cloud and AI services that other teams build and operate upon, including hosted runtimes, control planes, or developer operations tooling. • Demonstrated ability to integrate governance, risk, compliance, cybersecurity, and control requirements directly into technology products and operator workflows. • Strong understanding of identity and access controls, entitlements, delegation and OBO patterns, auditability, logging, observability, and secure integration patterns. • Familiarity with cloud deployment models across AWS or GCP, federated runtimes, gateways, and proxies. • Awareness of Responsible AI principles, including safety, fairness, and governance, and comfort delivering AI products within a regulated environment. • Understanding agentic patterns such as orchestration, tool use, multi-step reasoning, and human-in-the-loop design, with an appreciation for balancing autonomy and guardrails. • Experience with software asset registration, developer portals, system-of-record design, or service ownership models. Shape the future of enterprise agentic platform engineering by building the AI agent builder platform teams rely on.

Requirements

Must-haves

Develop a product strategy and vision for the agent platform and governance services so lifecycle operation, identity, and control become platform capabilities rather than wheels each engineering team reinvents.
Own, maintain, and develop a product backlog across ADLC, Agent Cloud, Managed Agents, the Control Tower, Gateway, Identity, and governance that supports the strategic roadmap and value proposition.
Deliver identity and delegation capabilities including multi-hop OBO downscoping, short-lived scoped credentials, and MCP and agent certification lifecycle.
Own governance as a service, including the Agent Manifest and release graph, and the Agent and MCP System of Record, with automatic evidence capture targeting the vast majority of governance evidence at build time.
Document the ADLC consistently across the SDK and other surfaces, and simplify Managed Agent onboarding during feasibility so governance steps enable adopters.
Embed controls for model usage, tool invocation, data access, entitlements, logging, auditability, and human oversight directly into the developer and operator workflow so compliance is the path of least resistance.
Build the framework and track key success metrics such as production conversion, operational overhead, governance automation rate, reliability, and control effectiveness.
Own end-to-end delivery and execution, planning and prioritizing the backlog of supporting scrum teams.
Partner with Legal, Compliance, Risk, Cybersecurity, Model Risk, and Data Governance to operationalize responsible AI and agentic AI requirements as product capabilities rather than manual gates.
Required qualifications, capabilities, and skills
5+ years of experience or equivalent expertise in AI product management or a relevant domain area.
Advanced knowledge of the Agent development life cycle and Spec Driven Development, design, and data analytics.
Proven ability to lead product life cycle activities including discovery, ideation, strategic development, requirements definition, and value management.
Demonstrated experience with, or a working understanding of, AI and agentic products or platforms, including foundational concepts such as LLMs and Harness.
Hands-on experience using LLMs and AI-powered tools to drive personal and team productivity, with a strong instinct for where AI can be applied to solve problems.
Familiarity with evaluating and instrumenting AI systems, including strong analytical skills to measure quality and performance for non-deterministic, agent-driven experiences.
Proven track record of delivery and new product feature creation through agile product development and the software delivery life cycle.
Strong stakeholder management and interpersonal skills, with strategic thinking, thought leadership, and the ability to influence and align cross-functional partners.
A bias for action and comfort operating in ambiguity, with the initiative to move quickly in a dynamic, fast-evolving environment.
Excellent written and oral communication skills.
Experience delivering platform, infrastructure, or cloud and AI services that other teams build and operate upon, including hosted runtimes, control planes, or developer operations tooling.
Demonstrated ability to integrate governance, risk, compliance, cybersecurity, and control requirements directly into technology products and operator workflows.
Strong understanding of identity and access controls, entitlements, delegation and OBO patterns, auditability, logging, observability, and secure integration patterns.

Nice-to-haves

Preferred qualifications, capabilities, and skills

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Keywords to mirror

aiscrumagileawsgcp

Platform: oracle-cloud
Effort: medium

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