Senior Manager of Software Engineering - Front office Technology

Join JPMorgan Chase's Treasury & Chief Investment Office Technology team and lead the development of mission-critical platforms that support global financial markets. We're seeking a hands-on engineering leader with expertise in Java or Python, AWS, cloud-native architectures, microservices, and AI-assisted software development to mentor high-performing teams, drive technical strategy, and deliver scalable, secure solutions. This is a unique opportunity to influence enterprise-wide engineering innovation, advance AI-enabled development practices, and make a significant impact at one of the world's leading financial institutions.

As a Senior Manager of Software Engineering at JPMorgan Chase within the Corporate Technology -Treasury & Chief Investment Office Technology team, you play a pivotal leadership role by providing advanced technology solutions both individually and as a technical coach and mentor to a team of talented and driven software engineers. Leveraging your expertise, you oversee essential technology initiatives across various technical domains within the Trade Execution technology team, supporting the business goals of the Treasury & Chief Investment Office (TCIO).

Job responsibilities

• Partner with the Front Office Desk to understand book-of-work requirements and develop solutions/proposals that improve operational efficiency.

• Serve as functional team manager for multiple programs within Treasury & Chief Investment Office (TCIO) Trade Execution Technology

• Provides guidance to the software engineers on daily tasks and activities to execute creative software solutions, including design, development, and technical troubleshooting, with the ability to think beyond routine or conventional approaches.

• Anticipates dependencies with other teams and leads evaluation sessions with external vendors and internal teams to drive outcome-oriented designs and ensure applicability within existing systems and information architecture.

• Manages stakeholder relationships and the team’s work in accordance with compliance standards, service level agreements, and business requirements

• 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.

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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.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5 years of applied experience

• Experience supporting trade execution platforms, with strong business knowledge of investment banking products including fixed income, mortgages, and derivatives.

• Strong track record of leading software engineering teams to deliver cutting-edge technology solutions

• Hands-on practical experience delivering system design, application development, testing, and operational stability

• Advanced in one or more programming language(s)- Java or Python

• Hands-on experience with AWS cloud services (e.g., EC2, S3, Lambda, RDS, etc.).

• Experience designing, developing, and deploying scalable applications in cloud environments.

• Experience with containerization and orchestration tools (e.g., Docker, Kubernetes).

• Experience with modern monitoring and logging tools (e.g., DataDog, Dynatrace, Splunk).

• 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.