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Director of Software Engineering (AIOps) - Executive Director
senior · Technology / Software Development
If you are a software engineering leader ready to take the reins and drive impact, we’ve got an opportunity just for you.
As a Director of Software Engineering at JPMorgan Chase within the Engineer's Observability Platforms team, you lead a technical area and drive impact within teams, technologies, and projects firm wide. Utilize your in-depth knowledge of software, applications, technical processes, and product management to drive multiple complex projects and initiatives, while serving as a primary decision maker for your teams and be a driver of AIOps innovation and solution delivery.
This is a greenfield opportunity to redevelop a critical alerting platform for the whole firm in-house, introducing AI generated self-heal code fixes or troubleshooting root cause analysis finding for application support teams.
Job responsibilities
- Leads technology and process implementations to achieve functional technology objectives in the Observability Platforms space, providing essential services for Site Reliability Engineers, Operations and Engineers across the whole firm
- Innovates, designs and delivers technical solutions that can be leveraged across multiple businesses and domains, this will include AIOps strategy for the future
- Sets direction and governance for agentic AI-enabled engineering and SDLC/TLM automation within a technical area to drive measurable improvements in speed, quality, and operational outcomes (e.g., AI-orchestrated delivery workflows, release readiness controls, automated test modernization, and incident triage acceleration), while establishing guardrails for validation, security, resiliency, traceability, and reuse 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 at scale.
- Makes decisions that influence teams’ resources, budget, tactical operations, and the execution and implementation of processes and procedures
- Carries governance accountability for coding decisions, control obligations, and measures of success such as cost of ownership, maintainability, and portfolio operations
- Influences peer leaders and senior stakeholders across the business, product, and technology teams
- Champions the firm’s culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
- Experience leading adoption of agentic AI-enabled engineering practices (using enterprise-authorized tools within the work environment) across teams, including defining operating expectations (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs.
- Strong understanding of responsible AI use and control expectations in engineering workflows, including data sensitivity, resiliency/security implications, and governance; ability to influence leaders on safe scaling patterns and reuse.
- Experience developing or leading teams of Java based developers, with a background in Java development. Demonstrated knowledge of software applications installation and configuration automation, through automation as code principles.
- Formal training or certification on software engineering concepts and expert applied experience. In addition, advanced experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise
- Hands-on practical experience in system design, application development, testing, and operational stability
- Demonstrated knowledge and implementation of AWS infrastructure or other cloud design and implementation
Preferred qualifications, capabilities, and skills
- Usage of large scale Observability Platforms (example products IBM Netcool, (Watson CloudPak AiOps), Tivoli, SCOM, SMARTS, Dynatrace, Splunk, Elastic, Prometheus, Grafana or Messaging systems for telemetry transport like Kafka, OTEL)
- Formal training or certification on software engineering concepts and expert applied experience. In addition, advanced experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise.
- Experience of researching and developing AIOps or ML solutions
- Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
- Overall knowledge of the Software Development Life Cycle
- Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- Experience with one or more observability platforms such as Prometheus, Grafana, Dynatrace, Datadog, Splunk etc.