Principal Technical Program Manager - AI Product Development Lifecycle

Elevate your career by steering multi-faceted tech programs, integrating innovative solutions for a dynamic impact across global operations. 
 
As a Principal Technical Program Manager in Operating Model Enablement, you will lead complex, multi-functional technology projects and programs that will impact experiences for multiple groups across the firm, including clients, employees, and stakeholders. Your advanced analytical reasoning and adaptability skills will enable you to break down business, technical, and operational objectives into manageable tasks, while navigating through ambiguity and driving change. With demonstrated technical fluency, you will effectively manage resources, budgets, and cross-functional teams to deliver innovative solutions that align with the firm’s strategic goals. Your exceptional communication and influencing abilities will foster productive relationships with stakeholders, ensuring alignment and effective risk management. In this pivotal role, you will contribute to the development of new policies and processes, shaping the future of our technology landscape. 

Job responsibilities

• Develop and implement strategic technical program plans, aligning 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 leveraging advanced technical fluency
• Champion continuous improvement by identifying process optimization opportunities, incorporating best practices, and staying abreast of emerging technologies
• Institutionalize the AI Product Development Lifecycle (AI PDLC), 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
• Uses enterprise-authorized AI capabilities within the work environment to accelerate program planning, dependency/risk synthesis, and executive-ready reporting, validating outputs and handling data according to sensitivity requirements.
• Promotes reuse-first, AI-assisted practices for program governance and continuous improvement routines, ensuring human review and alignment to delivery standards.

Required qualifications, capabilities, and skills

• 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 with 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 v…