Leads software engineering teams building Python and generative AI automation, governing application risk and compliance, and operating resilient production services. Responsibilities include Kubernetes and cloud architecture, data pipelines, observability, CI/CD, SRE practices, incident management, security, AI-assisted engineering adoption, and cross-team execution. The role also establishes service-level objectives, error budgets, and reliability improvements while coaching engineers and documenting organizational knowledge.
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.
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