Senior Lead Software Engineer- Manager

As a Senior Lead Software Engineer at JPMorgan Chase within Enterprise technology AI/ML Data Platforms team, you will be instrumental in building scalable, resilient and market-leading data solutions.

You will engage in root cause analysis, production changes, budgetary considerations, and staffing challenges.

Your experience will be vital in managing and mentoring team members to drive strategic change, both within your team and in partnership with colleagues across JPMorgan Chase & Co.'s global network of innovators.

Job Responsibilities:


* Expertise in application development and support with multiple technologies such as Databricks, Snowflake, AWS, Kubernetes, etc.


* Coordinate incident management coverage to ensure effective resolution of application issues.


* Collaborate with cross-functional teams to perform root cause analysis and implement production changes.


* Mentor and guide team members to foster innovation and strategic change.


* Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.


* Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.


* Develop and support AI/ML solutions for troubleshooting and incident resolution.

Required qualification, skills and capabilities:


* Proficient in site reliability culture and principles and familiarity with how to implement site reliability within an application or platform


* Proficiency in running production incident calls and managing incident resolution.


* Experience in observability such as white and black box monitoring, service level objective alerting, and telemetry collection using tools such as Grafana, Dynatrace, Prometheus, Datadog, Splunk, and others


* Strong understanding of SLI/SLO/SLA and Error Budgets


* Proficiency in Python or PySpark for AI/ML modeling.


* Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security


* Must be able to reduce toil by building new tools to automate repeated tasks.


* Hands-on experience in system design, resiliency, testing, operational stability, and disaster recovery


* Understanding of network topologies, load balancing, and content delivery networks.


* Strong understa...