Technical Project Manager - Sr. Lead Software Engineer

JPMorganChase•Jersey City, NJ

About The Position

At JPMorganChase, within the Commercial & Investment Bank FX/Cash Custody organization, the Senior Manager of Software Engineering provides technical coaching and advisory to multiple technical teams and anticipates dependencies across functions. The role uses deep technical expertise to influence budget and technical considerations and advance operational efficiency and functionality. The position provides direction, oversight, and coaching to software engineers working on basic to moderately complex tasks, and is accountable for decisions affecting team resources, budgets, tactical operations, and process execution. It establishes and scales practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across teams, with measurable delivery, quality, and operational outcomes. The role promotes secure, resilient, validated, and reusable patterns; applies software development lifecycle toolchain capabilities to improve efficiency; and prioritizes reuse of existing firm technology assets. The role also leads software development projects and multi-team adoption of authorized AI-assisted tools, coordinates timelines, backlogs, and dependencies, and ensures collaboration among teams and stakeholders. It identifies and mitigates delivery issues, escalates as necessary, and advises leadership on budgets, approaches, and technical considerations.

Requirements

Formal training or certification in software engineering concepts.
At least 5 years of applied experience in software engineering concepts.
At least 2 years of experience leading technologists to manage and solve complex technical items within the relevant domain.
Experience leading projects.
Strong technical background with prior hands-on software engineering experience in coding, designing, and building.
Technical program and project management experience in software development, including managing timelines, backlogs, and dependencies across stakeholder groups.
Experience with technical and functional analysis and design, refining requirements and designs to comply with patterns established in cash architecture.
An AI-first mindset, using AI to codify thinking tasks and streamline program delivery.
Understanding of responsible AI use in engineering workflows, including data sensitivity, resiliency and security implications, and control expectations; ability to coach managers and leads and influence leaders on safe scaling patterns.
Ability to guide and coach teams toward goals aligned with strategic initiatives.
Experience leading multi-team adoption of enterprise-authorized AI-assisted development and delivery tools, including defining governance and ways of working, measuring outcomes, and ensuring secure handling of sensitive inputs and outputs.
In-depth knowledge of the services industry and its IT systems.
Practical cloud-native experience.

Nice To Haves

Experience working at code level.

Responsibilities

Provide overall direction, oversight, and coaching to entry-level and mid-level software engineers working on basic to moderately complex tasks.
Make decisions that influence team resources, budgets, tactical operations, and the execution and implementation of processes and procedures.
Establish and scale operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across multiple teams.
Set measurable expectations for delivery throughput, defect reduction, reliability, and operational outcomes; ensure validation, security, resiliency, and reuse of proven patterns.
Apply software development lifecycle toolchain capabilities, including authorized AI-assisted development and automation, to improve efficiency and support capacity-unlock initiatives, prioritizing existing firm technology assets.
Ensure successful collaboration across teams and stakeholders.
Identify and mitigate issues affecting the book of work and escalate issues as necessary.
Provide leadership with input on budgets, approaches, and technical considerations to improve operational efficiency and functionality.
Lead software development projects, managing timelines, backlogs, and dependencies across stakeholder groups.
Guide and coach teams on approaches that align with strategic initiatives.
Lead multi-team adoption of enterprise-authorized AI-assisted development and delivery tools, including governance, ways of working, human-in-the-loop validation, quality gates, and outcome measurement.
Promote secure handling of sensitive inputs and outputs and responsible AI practices in engineering workflows.