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 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 reusable building blocks that every agent at the firm is assembled from: agent memory, harness as the firm-grade runtime for long-running agents, reusable agents such as NL2SQL shipped as configurable templates, and the top connectors that let agents act in the systems people work in. You are accountable for converging fragmented, bespoke implementations onto shared, governed components that teams configure rather than rebuild, so the platform scales through reuse instead of per-team builds. Job responsibilities • Develop a product strategy and vision for reusable agent building blocks, so teams compose governed, versioned components rather than rebuilding memory, runtime, and connectivity from scratch. • Own, maintain, and develop a product backlog across memory, harness, reusable agents, and top connectors that support the strategic roadmap and value proposition. • Engineer agentic memory as one governed memory-as-a-service platform with multiple constructs, including episodic and semantic recall with entitlement-aware scoping, enabled per use case. • Make the Harness the firm-grade runtime for long-running agents, and ship reusable agents as configurable templates on it rather than bespoke runtimes. • Drive reusable agents consolidation onto one platform, including a fine-tuned model in production and UI-based fine-tuning. • Execute the top-connector backlog, including connectors such as M365, ServiceNow, and Salesforce, and offer them to Directories as distributed, governed assets without duplicate builds. • Partner with data platform and engineering teams to deliver secure, governed access to structured, unstructured, analytical, and real-time data in line with firm data policy. • Build the framework and track key success metrics such as component reuse over bespoke builds, memory convergence, connector adoption, and reliability. • Capture the voice of the customer from lines of business and translate recurring needs into reusable components that serve many teams rather than one. • Own end-to-end delivery and execution, planning and prioritizing the backlog of supporting scrum teams. • Shape product strategy and vision as a thought leader who influences stakeholders across Technology, analytics, and the business and champions adoption of shared building blocks. 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 defining or managing reusable technology assets such as components, templates, connectors, or shared services that many teams build upon. • Working understanding of retrieval-augmented generation, agent memory, prompt and context management, and structured data access such as natural language to SQL. • Familiarity with data integration and connector patterns across sources such as M365, ServiceNow, Salesforce, Snowflake, and Databricks, including entitlements and secure access. • 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. • Exposure to model fine-tuning, evaluation of retrieval and query quality, and vector stores. • Awareness of Responsible AI principles, including safety, fairness, and governance, and comfort delivering AI products within a regulated environment. • Experience benchmarking reusable components such as memory, retrieval, or NL2SQL against internal targets and external industry benchmarks, using the results to measure quality and performance and to set roadmap priorities. You shape the future of enterprise agentic platform engineering by delivering an AI agent builder platform engineers depend on daily.

Requirements

Must-haves

Develop a product strategy and vision for reusable agent building blocks, so teams compose governed, versioned components rather than rebuilding memory, runtime, and connectivity from scratch.
Own, maintain, and develop a product backlog across memory, harness, reusable agents, and top connectors that support the strategic roadmap and value proposition.
Engineer agentic memory as one governed memory-as-a-service platform with multiple constructs, including episodic and semantic recall with entitlement-aware scoping, enabled per use case.
Make the Harness the firm-grade runtime for long-running agents, and ship reusable agents as configurable templates on it rather than bespoke runtimes.
Drive reusable agents consolidation onto one platform, including a fine-tuned model in production and UI-based fine-tuning.
Execute the top-connector backlog, including connectors such as M365, ServiceNow, and Salesforce, and offer them to Directories as distributed, governed assets without duplicate builds.
Partner with data platform and engineering teams to deliver secure, governed access to structured, unstructured, analytical, and real-time data in line with firm data policy.
Build the framework and track key success metrics such as component reuse over bespoke builds, memory convergence, connector adoption, and reliability.
Capture the voice of the customer from lines of business and translate recurring needs into reusable components that serve many teams rather than one.
Own end-to-end delivery and execution, planning and prioritizing the backlog of supporting scrum teams.
Shape product strategy and vision as a thought leader who influences stakeholders across Technology, analytics, and the business and champions adoption of shared building blocks.
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 defining or managing reusable technology assets such as components, templates, connectors, or shared services that many teams build upon.

Nice-to-haves

Preferred qualifications, capabilities, and skills

Application prep

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Keywords to mirror

aiscrumagilesql

Platform: oracle-cloud
Effort: medium

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