Senior Manager of Software Engineering - Java, Distributed Platforms & AI/Agentic Engineering

Join us to shape the future of financial technology through impactful leadership and technical guidance. Advance your career by mentoring teams and influencing innovative solutions.

As a Senior Manager of Software Engineering at JPMorgan Chase within the Infrastructure Platforms, you serve in a leadership role by providing technical coaching and advisory for multiple technical teams, as well as anticipate the needs and potential dependencies of other functions within the firm. As an expert in your field, your insights influence budget and technical considerations to advance operational efficiencies and functionalities.

Job responsibilities

• Provide direction, oversight, and coaching for entry-level to mid-level software engineers delivering basic to moderately complex work, while fostering a culture of opportunity and respect.
• Own accountability for key decisions impacting resources, budget, tactical operations, and process implementation; provide input to leadership on budget, approach, and technical considerations to improve efficiency and functionality.
• Advocate and drive adoption of firmwide SDLC frameworks, tools, and practices, ensuring consistent execution and continuous improvement across teams.
• Ensure effective collaboration across teams and stakeholders, aligning priorities, expectations, and delivery outcomes.
• Identify, mitigate, and escalate risks/issues as needed to successfully execute a defined book of work.
• Set and scale operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across multiple teams-establishing measurable expectations (e.g., throughput, defect reduction, reliability) and enforcing validation, security, resiliency, and reuse of proven patterns.
• Apply SDLC toolchain expertise (including enterprise-authorized AI-assisted development and automation) to drive efficiency and capacity-unlock initiatives, prioritizing reuse of existing firm technology assets.
• Lead technical design and delivery of modern Java-based platforms and self-service solutions-partnering with SRE to embed observability, reliability, resiliency, automation, and controls; guiding API design, workflow orchestration (including agentic solutions), production readiness, and solutions spanning file/block/object storage and data-transfer technologies.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive efficiency and support capacity unlock initiatives across teams, prioritizing reuse of existing firm technology assets.
• Sets and scales operating practices for enterprise-authorized AI-assisted engineering and SDLC/TLM automation across multiple teams to improve delivery speed, quality, and operational outcomes; establishes measurable expectations (e.g., throughput, defect reduction, reliability) and ensures consistent validation, security, resiliency, and reuse of proven patterns.

• Formal training or certification on software engineering concepts and 5+ years applied experience . In addition, 2 + years of experience leading technologists to manage and solve complex technical items within your domain of expertise
• Minimum of 10 years of experience leading software teams
• Hands-on expertise in Java and related enterprise frameworks, including Spring and Spring Boot, with experience designing and supporting RESTful APIs and distributed services.
• Ability to guide and coach teams to achieve strategic goals
• Practical experience delivering system design, application development, testing, and operational stability
• Experience leading multi-team adoption of enterprise-authorized AI-assisted development and delivery tools, including defining governance/ways of working (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and control expectations; ability to coach managers/leads and influence leaders on safe scaling patterns.
• Experience with hiring, developing, and recognizing talent

• Experience building and operating AI-assisted engineering workflows & AIOps
• Hands-on experience delivering LLM-enabled features (e.g., natural-language query, guided troubleshooting, intelligent self-service) including: RAGs & agentic workflows
• Experience building cloud native solutions such as AWS