Principal Technical Program Manager - AI Product Development Lifecycle
$194.8k - $285k
Expert
Plano, TX, USA+2 moreMore locations: Jersey City, NJ, USA | Wilmington, DE, USA
In Person
Project & Program ManagementProgram Management
About the job
Requirements
- At least 7 years of experience or equivalent expertise in technical program management, leading complex technology projects and programs in large organizations.
- Demonstrated 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 using 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 engineering teams on security, scalability, reliability, and production readiness.
- Deep understanding of how AI-native delivery differs from traditional software development, 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.
- Demonstrated success partnering with model risk, technology risk, cybersecurity, compliance, and audit functions in a regulated environment to embed controls into AI products and engineering workflows.
- Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support 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, escalating when uncertain and following data-handling expectations.
Responsibilities
- Develop and implement strategic technical program plans aligned with organizational goals and cross-functional collaboration, and oversee complex program execution by managing resources, budgets, and timelines while mitigating risks and addressing roadblocks.
- Guide the selection and implementation of appropriate technologies, platforms, and software tools using advanced technical fluency.
- Identify process-optimization opportunities, incorporate best practices, and stay abreast of emerging technologies to champion continuous improvement.
- 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.
- Expand and scale the agentic engineering operating model by establishing reference architectures, shared tooling, evaluation harnesses, guardrail patterns, and observability standards for production-grade agentic systems.
- 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.
- Drive adoption of the path from AI idea generation to production by applying value stream mapping, flow metrics, work-in-progress limits, queue analysis, and lean methods to remove handoffs, duplicated reviews, and other sources of coordination cost.
- Partner with model risk, technology risk, cybersecurity, compliance, and audit as design partners to embed governance and automated control evidence directly into AI engineering and delivery workflows.
- Advise senior technology leaders on where agentic systems create value or risk, translate AI investments into measurable outcomes, and use cycle time, throughput, change failure rate, evaluation quality, and risk posture to guide decisions.
- Use enterprise-authorized AI capabilities within the work environment to accelerate 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 routines, ensuring human review and alignment to delivery standards.
Desired Qualifications
- Experience defining or implementing an enterprise AI Product Development Lifecycle, AI engineering standards, reference architectures, evaluation frameworks, or reusable patterns adopted across multiple products, platforms, or engineering teams.
- Experience designing or operating agentic workflows with orchestration frameworks, Model Context Protocol, 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.
- Demonstrated 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.
About the company
JPMorgan Chase runs the largest bank in the United States, which serves households and small businesses as Chase and large companies, governments, and investors as J.P. Morgan. During the 2008 financial crisis it bought the failing investment bank Bear Stearns and lender Washington Mutual, and in 2023 it took over First Republic Bank after regulators seized it. Chase takes deposits and earns interest on credit cards, mortgages, and car loans, while the investment side earns fees for advising on mergers, raising money for companies, trading stocks and bonds, and managing wealth. Formed in a 2000 merger, JPMorgan Chase traces its roots to 1799 and is based in New York City.
Company Size
10,001+
Company Stage
IPO
Headquarters
New York City, New York
Founded
1799
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Simplify's Take
What believers are saying
- Growth is broad-based: every JPMorgan division posted record revenue in the June 2026 quarter, and trading jumped 35%.
- Stock grants gained value: shares rose 21% in three months, valuing JPMorgan at $965 billion in August 2026.
- Tech teams are well funded: JPMorgan's nearly $20 billion yearly tech budget is the largest in banking.
What critics are saying
- AI is shrinking some teams: Dimon said in July 2026 that AI cut up to 40% of certain roles.
- Plan on five office days a week: JPMorgan ended hybrid schedules for all staff in early 2025.
- It is fighting the president in court: Trump sued JPMorgan for $5 billion in January 2026 over his closed accounts.
What makes JP Morgan Chase unique
- It ranks first in investment banking: JPMorgan earned 9.3% of global deal fees in the first half of 2026.
- It trains future CEOs: 10 Fortune 500 chiefs were JPMorgan alumni in September 2025, fifth-most of any employer.
- Its payments arm is a hidden giant: JPMorgan says it processes $12 trillion a day across 160-plus countries.
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Benefits
- Health Insurance
- Flexible Work Hours
- Paid Sick Leave
- Paid Holidays