About this role
As Manager of Software Engineering at JPMorgan Chase within the Corporate Technology, 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
• Design, code, test, and deliver automation (including LLMs/agents) to eliminate manual operational work and streamline AO workstreams’ remediations (e.g., control items, security vulnerabilities, upgrades, and FARM findings). • Govern application risk, controls, and compliance: own adherence to firm standards, partner with Technology Risk & Controls, manage Technology Lifecyle Management (TLM), and drive closure of issues/findings (e.g., FARM) through effective remediation and evidence management. • Own security and data accountability for the application: ensure strong authentication/authorization, vulnerability and certificate hygiene, and proper data registration/classification plus compliant storage/retention/disposal. • Coordinate across product and engineering at scale to prioritize and drive execution with a clear sense of urgency for AO workstreams (including influencing/coaching teams and aligning execution across large developer communities). • 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. • Run resilient, well-operated production services end-to-end: implement monitoring/logging and anomaly detection, maintain secure network configurations/least privilege, and lead/support incident/problem/change management and recovery/resiliency readiness. • Demonstrates and champions site reliability culture and practices and exerts technical influence throughout your team • Leads initiatives to improve the reliability and stability of your team’s applications and platforms using data-driven analytics to improve service levels • Collaborates with team members to identify comprehensive service level indicators and stakeholders to establish reasonable service level objectives and error budgets with customers • Documents and shares knowledge within your organization via internal forums and communities of practice Required qualifications, capabilities, and skills
• Bachelor’s degree (or equivalent experience) in a software engineering discipline with 8+ years of experience. • Expertise in at least one technology stack with a track record of designing, coding, testing, and delivering production software. • Strong experience with Kubernetes, AWS/other cloud platforms, and Big Data/ETL pipelines (e.g., Hortonworks/AWS), including scalable data processing solutions. • Strong development experience in Java, Python, or Scala, with excellent debugging and troubleshooting skills for complex production issues. • 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. • Working knowledge of core infrastructure components (routers, load balancers, cloud products, containers, compute, storage, networks) and ability to solve complex, mission-critical problems across domains. • Deep proficiency in SRE best practices: reliability, scalability, performance, security, enterprise system architecture, and toil reduction; able to implement within an application or platform. • Deep knowledge of software applications and technical processes, with emerging depth in one or more technical disciplines. • Proficiency in observability (white/black box monitoring, SLO alerting, telemetry collection) using tools such as Grafana, Dynatrace, Prometheus, Datadog, Splunk, etc. • Proficiency in CI/CD tools (e.g., Jenkins, GitLab, Terraform), plus ability to troubleshoot networking issues, solve data structure/algorithm problems, teach new languages, and collaborate across stakeholder levels. Preferred qualifications, capabilities, and skills
• Certified in Python , Gen AI.