IT Project Manager IV – AI & Agent-Driven Engineering Transformation

Wilson•Mountain View, CA

2d•Hybrid

About The Position

Our client is a leading global technology and financial services organization undergoing a major transformation in how its engineering teams build, test, and deliver software. The organization is advancing from traditional AI-assisted development toward an Agent-Driven Development model, using AI agents and intelligent engineering workflows to significantly improve developer productivity, accelerate delivery, and transform the software development operating model. We are seeking an experienced IT Project Manager / Technical Program Manager (TPM) who can operate at both the strategic and technical levels. This individual will help translate an ambitious AI strategy into an executable, measurable transformation across a complex Fintech engineering organization. The ideal candidate is a systems thinker, strong program leader, and experienced change agent who can bring together engineering, product, design, architecture, data, and platform teams to drive adoption and measurable business outcomes.

Requirements

Proven experience leading large-scale technical programs or transformation initiatives within complex organizations.
Strong understanding of the end-to-end software development lifecycle, including engineering, architecture, testing, deployment, and operational processes.
Experience managing programs involving significant organizational change, technology adoption, and operating-model transformation.
Strong understanding of modern AI/ML development concepts and emerging AI-assisted or agent-driven software development practices.
Experience working closely with engineering, architecture, product, platform, and data teams.
Demonstrated ability to manage multiple workstreams, dependencies, risks, and competing priorities simultaneously.
Strong stakeholder-management skills with the ability to influence senior leaders and technical teams without direct authority.
Experience defining, tracking, and communicating program success metrics and engineering productivity KPIs.
Strong analytical and problem-solving skills with a data-driven approach to decision-making.
Excellent written and verbal communication skills, including executive-level presentations and program reporting.
Experience leading change-management, training, enablement, or adoption initiatives in a technology environment.

Nice To Haves

Experience with AI agents, agentic workflows, generative AI, or AI-enabled software development.
Experience with developer productivity platforms and AI coding assistants such as Claude Code, Cursor, GitHub Copilot, or similar technologies.
Familiarity with Model Context Protocol (MCP) or comparable approaches for enabling AI agents to interact with software services and enterprise systems.
Experience establishing technology maturity models, adoption frameworks, or engineering productivity measurement programs.
Experience working in Fintech, financial services, payments, banking, or other high-integrity transaction environments.
Experience working with external AI/technology vendors and coordinating vendor-led enablement programs.
Technical background in software engineering, architecture, product development, or technology program management.

Responsibilities

Lead complex, cross-functional programs supporting the organization's transition toward agent-driven software development.
Translate AI and engineering strategy into actionable roadmaps, workstreams, milestones, and measurable outcomes.
Facilitate cross-functional discussions and drive alignment across engineering, product, design, architecture, platform, and enablement teams.
Identify dependencies, risks, blockers, and opportunities across multiple teams and business units.
Drive adoption of AI-powered development practices and workflows across the engineering organization.
Partner with architecture and engineering leaders to ensure applications and services are structured to support intelligent agents and emerging AI capabilities.
Coordinate the rollout of AI development tools and capabilities, including tools such as Claude Code CLI and Cursor, along with AI-powered solutions for code review, build-failure analysis, and artifact generation.
Help establish repeatable processes and operating models that enable teams to incorporate AI agents into their day-to-day development lifecycle.
Own end-to-end program execution across multiple workstreams and stakeholder groups.
Coordinate activities across engineering, product management, design, platform, architecture, data & analytics, and external technology partners.
Establish program governance, operating rhythms, status reporting, milestones, and executive-level communications.
Ensure critical information, decisions, risks, and dependencies are communicated effectively across the organization.
Act as a central point of coordination between leadership and execution teams.
Partner with Data & Analytics teams to establish meaningful metrics for measuring AI and agent adoption and engineering productivity.
Track metrics such as tool adoption/usage, developer engagement, development cycle time, and developer time saved.
Help develop maturity frameworks to measure the organization's progression toward increasingly sophisticated AI-enabled development practices.
Support leadership reporting and dashboards that provide visibility into adoption, maturity, productivity improvements, and program outcomes.
Help drive measurable efficiency improvements through systematic adoption of AI-enabled workflows.
Develop and execute change-management strategies to support sustained adoption of new AI-enabled development practices.
Coordinate training sessions, workshops, communications, and enablement programs for technical and non-technical stakeholders.
Work with internal teams and external technology partners to deliver education and hands-on learning opportunities.
Identify adoption barriers and resistance, develop mitigation strategies, and help teams successfully transition to new ways of working.
Establish mechanisms to capture feedback from early adopters and identify successful practices that can be scaled across the organization.
Help identify and document reusable AI prompts, workflows, orchestration patterns, and development practices.
Continuously evaluate what is working, what needs to change, and how successful approaches can be institutionalized across the organization.