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.