This is your chance to change the path of your career and guide multiple teams to success at one of the world's leading financial institutions.
As a Manager of Software Engineering at JPMorganChase within the Commercial & Investment Bank, you lead multiple teams and manage day-to-day implementation activities by identifying and escalating issues and ensuring your team's work adheres to compliance standards, business requirements, and tactical best practices.
Job responsibilities - Provides guidance to immediate team of software engineers on daily tasks and activities
- Sets the overall guidance and expectations for team output, practices, and collaboration
- Leads team adoption of enterprise-authorized AI-assisted engineering practices and SDLC/TLM automation to improve delivery speed, quality, and operational outcomes, while setting expectations for human validation, secure handling of inputs/outputs, and consistent use of reusable patterns across teams.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation and support capacity unlock initiatives.
- Anticipates dependencies with other teams to deliver products and applications in line with business requirements
- Manages stakeholder relationships and the team's work in accordance with compliance standards, service level agreements, and business requirements
Required qualifications, capabilities, and skills - Formal training or certification on software engineering concepts and 5+ years applied experience.
- Experience in demonstrated coaching and mentoring experience.
- Experience in leading technology projects, managing technologists.
- Experience leading responsible adoption of enterprise-authorized AI-assisted development and delivery tools across engineering teams, including defining ways of working (review/validation expectations), measuring outcomes, and ensuring secure handling of data.
- Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, resiliency/security implications, and governance expectations; ability to coach engineers on compliant and effective usage.
- Proficient in all aspects of the Software Development Life Cycle
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- In-depth knowledge of the financial services industry and their IT systems
- Practical cloud native experience
Preferred qualifications, capabilities, and skills - Backend - Strong Expertise and Hands-on Knowledge on below, Strong understanding of algorithms. Python, Data backbones (Airflow, Spark). Core Database concepts (Relational & Document). Cloud technologies
AI/ML technologies (Gen AI/LLMs, Agentic AI etc) - Frontend Javascript/typescript, React/Next.
- Observability Skillsets - Open Telemetry (agent setup and instrumentation patterns), Python and Java (instrumentation and troubleshooting), Dynatrace (dashboards, DQL), Splunk (log analytics/search), Grafana (dashboarding), Geneos (alerting) and Prometheus (time-series metrics).
