Director of Software Engineering - Machine Learning/AI

JP Morgan Chase; Scotland; On-site; GBP 150,000 - 210,000; Full time

Job summary

JPMorganChase is seeking a Director of Software Engineering within Wholesale Credit Risk Technology. You will lead a technical area, drive complex projects, and act as a primary decision maker, delivering innovative, AI-enabled engineering solutions across departments.

The role requires proven leadership across cross-functional teams, cloud-native capabilities, and governance of responsible AI; you will influence senior stakeholders and set the technical direction for scalable delivery.

Qualifications

• Formal training or certification in software engineering concepts.
• Experience leading cross-functional technologists.
• Experience hiring, developing, recognizing talent.
• Experience leading adoption of agentic AI-enabled engineering practices.

Responsibilities

• Lead technology and process implementations to meet objectives.
• Make governance decisions affecting resources, budget, and delivery.
• Set direction for AI-enabled engineering and SDLC automation across a technical area.
• Apply enterprise AI tools to improve automation value and capacity.
• Ensure security, resiliency, and traceability in engineering work.
• Deliver reusable technical solutions across businesses and domains.
• Influence senior stakeholders across product, technology and business groups.

Skills

Software engineering concepts

Cross-functional leadership

Talent development

Agentic AI adoption

Cloud-native experience

Responsible AI governance

Education

Expertise in Computer Science/Engineering/Mathematics

Tools

AI-assisted development tools

Job description

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 JPMorganChase within Wholesale Credit Risk Technology, you lead a technical area and drive impact within teams, technologies, and projects across departments. 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 innovation and solution delivery.

Job responsibilities

• Leads technology and process implementations to achieve functional technology objectives
• Accountable for decisions that influence teams’ resources, budget, tactical operations, and the execution and implementation of processes and procedures
• 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.
• Carries governance accountability for coding decisions, control obligations, and measures of success such as cost of ownership, maintainability, and portfolio operations
• Delivers technical solutions that can be leveraged across multiple businesses and domains
• Influences peer leaders and senior stakeholders across the business, product, and technology teams

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and advanced applied experience
• Experience developing or leading cross-functional teams of technologists
• Experience with hiring, developing, and recognizing talent
• 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.
• Practical cloud native experience
• Expertise in Computer Science, Computer Engineering, Mathematics, or a related technical field

Preferred qualifications, capabilities, and skills

• Worked on agentic platforms