Agentic AI Director of Software Engineering

JPMorgan Chase & Co. • Columbus, OH

2d

About The Position

As an Executive Director, Applied AI Engineering (Data Platform) within JPMorganChase Corporate Technology, this role leads the design, delivery, and scaling of production-grade AI agent capabilities across a Databricks-based data platform on Amazon Web Services. The position owns the strategy and technical direction for AI-enabled workflows that accelerate data modeling, pipeline generation, data classification, and governed self-service access, working closely with Information Architecture, Data Engineering, Data Governance, and business stakeholders.

The role owns the Applied AI roadmap and translates business needs into reusable platform capabilities. It sets standards and governance for agentic AI-enabled engineering and Software Development Life Cycle automation, with the aim of improving speed, quality, and operational outcomes. It establishes guardrails for validation, security, resiliency, traceability, and reuse, and communicates delivery progress, risks, and investment considerations to senior leaders. It also hires and develops an inclusive engineering team.

The position applies enterprise-authorized AI-assisted development and automation tools across the Software Development Life Cycle to improve automation value and support capacity unlock initiatives. AI pipelines and workflows must be production-ready, monitored, reliable, secure, and subject to human review and quality gates so outputs remain validated and traceable.

Requirements

• Formal training or certification in software engineering concepts and 10+ years of applied experience.
• At least 5 years of experience leading technologists to manage, anticipate, and solve complex technical items within the area of expertise.
• At least 10 years of demonstrated experience leading engineering teams and owning technical roadmaps for platform capabilities used by multiple product teams.
• Hands-on Databricks expertise, including Unity Catalog, Delta Lake, Spark, and SQL Warehouses, and the ability to set secure, scalable patterns.
• Strong LLM application architecture skills, including RAG, tools, agents, and orchestration, with a focus on safety and reliability.
• Experience building and running production AI systems with strong testing, observability, and resiliency practices.
• Strong knowledge of data governance and access controls, such as Immuta or similar, with audit-ready practices.
• Ability to translate technical decisions into business value, trade-offs, risks, and investment needs for senior leaders.
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment for tasks such as coding, test creation, troubleshooting, or documentation, and ability to critically evaluate and validate AI-generated outputs.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and adherence to resiliency and security expectations.
• Ability to influence leaders on safe scaling patterns and reuse.

Nice To Haves

• Experience building Model Context Protocol servers or secure agent tool layers.
• Familiarity with star schema and Medallion architecture to validate AI-generated outputs.
• Experience delivering AI in regulated environments with strong governance and auditability.
• Familiarity with Sigma Computing or similar Databricks-based analytics tools.
• Experience building reusable agent patterns and platform accelerators that scale across teams.

Responsibilities

• Own the Applied AI roadmap and translate business needs into platform capabilities.
• Scale agent workflows for data modeling, pipeline creation, data quality, and classification.
• Build a secure integration layer, including Model Context Protocol tooling where relevant, with controlled access and audit trails.
• Set standards for agent use of Databricks, Unity Catalog, Delta Lake, and policy controls.
• Ensure AI pipelines are production-ready, with strong performance, reliability, and monitoring.
• Implement human review and quality gates so outputs are validated and traceable.
• Deliver across multiple workstreams and communicate risks and progress to senior leaders.
• Hire and grow a high-performing, inclusive engineering team.
• Set direction and governance for agentic AI-enabled engineering and Software Development Life Cycle automation to improve speed, quality, and operational outcomes.
• Establish guardrails for validation, security, resiliency, traceability, and reuse across teams.
• Apply enterprise-authorized AI-assisted development and automation capabilities to improve automation value and support capacity unlock initiatives at scale.

Benefits

• Competitive total rewards package, with base salary determined by role, experience, skill set, and location.
• Eligible roles may receive commission-based pay and/or discretionary incentive compensation in cash and/or forfeitable equity.
• Comprehensive health care coverage.
• On-site health and wellness centers.
• Retirement savings plan.
• Backup childcare.
• Tuition reimbursement.
• Mental health support.
• Financial coaching.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed