Principal Technical Program Manager - AI Product Development Lifecycle

JPMorgan Chase & Co. • Jersey City, NJ

7d • $194,750 - $285,000

About The Position

As a Principal Technical Program Manager in Operating Model Enablement, you will lead complex, multi-functional technology programs that affect clients, employees, and stakeholders across the firm. You will translate business, technical, and operational objectives into manageable work, navigate ambiguity, drive change, and manage resources, budgets, and cross-functional teams to deliver solutions aligned with strategic goals. The role requires technical fluency, strong communication and influencing skills, effective stakeholder alignment and risk management, and contributions to new policies and processes.

The position focuses on institutionalizing the AI Product Development Lifecycle (AI PDLC) and scaling the agentic engineering operating model. You will lead multi-quarter programs across product, engineering, architecture, data, risk, controls, and operations; improve the path from AI concepts to production using lean methods and delivery metrics; and partner with governance and assurance functions to integrate controls into AI workflows. You will also advise senior technology leaders on the value and risks of agentic systems and use enterprise-authorized AI capabilities to support program work, validating outputs and observing data-sensitivity requirements.

JPMorganChase describes itself as a financial institution serving consumers, businesses, and government and institutional clients. It offers a competitive total rewards package, with compensation based on role, experience, skills, and location, plus benefits and programs subject to eligibility. The company is an equal opportunity employer and provides reasonable accommodations.

Requirements

• At least 7 years of experience or equivalent expertise in technical program management, leading complex technology projects and programs in large organizations.
• Experience designing, building, deploying, or governing production-grade generative AI and agentic systems, including tool use, orchestration, evaluation, guardrails, observability, and human oversight.
• Advanced hands-on experience with Claude Code, GitHub Copilot, and GitHub-based engineering workflows for prompt and context design, code generation, testing, debugging, code review, documentation, and workflow automation.
• Strong AI and software engineering depth, including the ability to read and critique code, assess architecture and implementation trade-offs, and challenge teams on security, scalability, reliability, and production readiness.
• Deep understanding of AI-native delivery, including non-deterministic testing, prompt and data versioning, evaluation gates, model and behavior drift, and continuous monitoring.
• Proven ability to lead complex, multi-quarter transformation programs across autonomous technology organizations and influence senior product, engineering, data, risk, and control stakeholders without direct authority.
• Experience applying lean product development and process-reengineering methods, including value stream mapping, flow, work-in-progress limits, queue and handoff analysis, and outcome-based delivery metrics.
• Experience partnering with model risk, technology risk, cybersecurity, compliance, and audit functions in a regulated environment to embed controls into AI products and engineering workflows.
• Experience using enterprise-authorized AI capabilities for technical program management workflows, with strong validation habits and awareness of data sensitivity.
• Ability to review and validate AI-assisted plans, risks, and recommendations before use, escalate when uncertain, and follow data-handling expectations.

Nice To Haves

• Experience defining or implementing an enterprise AI PDLC, AI engineering standards, reference architectures, evaluation frameworks, or reusable patterns adopted across multiple products, platforms, or engineering teams.
• Experience designing or operating agentic workflows using orchestration frameworks, Model Context Protocol (MCP), tool and API integrations, retrieval and context engineering, structured evaluation, and secure access to enterprise systems.
• Recent applied AI, machine learning platform, or agentic engineering experience with responsibility for production outcomes, reliability, monitoring, or value realization.
• A portfolio of AI-native prototypes, reference implementations, engineering accelerators, playbooks, internal standards, or communities of practice that progressed into sustained use.
• Background spanning technology strategy, program leadership, operating-model transformation, engineering productivity, and large-scale modernization in a regulated enterprise.
• Recognized thought leadership in AI engineering, agentic systems, software delivery transformation, enterprise operating models, or emerging technology adoption.

Responsibilities

• Develop and implement strategic technical program plans aligned with organizational goals and cross-functional collaboration.
• Oversee complex program execution, managing resources, budgets, and timelines while mitigating risks and addressing roadblocks.
• Guide the selection and implementation of technologies, platforms, and software tools.
• Identify process optimization opportunities, incorporate best practices, and stay current with emerging technologies.
• Institutionalize the AI Product Development Lifecycle, including use case intake, data and prompt versioning, evaluation gates, human-in-the-loop design, model risk integration, deployment, drift monitoring, and retirement.
• Scale the agentic engineering operating model through reference architectures, shared tooling, evaluation harnesses, guardrail patterns, and observability standards.
• Lead multi-quarter, cross-organizational programs that accelerate AI-native delivery across product, engineering, architecture, data, risk, controls, and operations teams without direct line authority.
• Improve the path from AI idea generation to production using value stream mapping, flow metrics, work-in-progress limits, queue analysis, and lean methods to reduce handoffs, duplicated reviews, and coordination costs.
• Partner with model risk, technology risk, cybersecurity, compliance, and audit to embed governance and automated control evidence into AI engineering and delivery workflows.
• Advise senior technology leaders on the value and risks of agentic systems, translate AI investments into measurable outcomes, and use delivery and risk metrics to guide decisions.
• Use enterprise-authorized AI capabilities for program planning, dependency and risk synthesis, and executive-ready reporting, validating outputs and handling data according to sensitivity requirements.
• Promote reuse-first, AI-assisted practices for program governance and continuous improvement, with human review and alignment to delivery standards.

Benefits

• Competitive total rewards package
• Comprehensive health care coverage
• On-site health and wellness centers
• Retirement savings plan
• Backup childcare
• Tuition reimbursement
• Mental health support
• Financial coaching
• Commission-based pay and/or discretionary incentive compensation may be available for eligible roles.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Education Level: No Education Listed