Agentic AI Director of Software Engineering

J.P. Morgan Chase & Co.; Ohio; On-site; USD 250,000 - 350,000; Full time

Job summary

JPMorganChase in the United States seeks an Executive Director, Applied AI Engineering (Data Platform) to lead the design, delivery, and scaling of production-grade AI agent capabilities on a Databricks-based data platform on AWS. You will own the AI roadmap, drive cross-team collaboration, ensure security controls, and build a high-performing engineering team while communicating risks to senior leaders.

This role combines strategic leadership with hands-on architecture to accelerate data

Qualifications

• Formal training or certification on software engineering concepts; 10+ years applied experience.
• 10+ years leading engineering teams and owning technical roadmaps for platform capabilities.
• Hands-on Databricks expertise (Unity Catalog, Delta Lake, Spark, SQL Warehouses) and ability to set secure, scalable patterns.
• Strong LLM application architecture skills (RAG, tools, 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 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 with demonstrated ability to evaluate AI outputs.
• Strong understanding of responsible AI use in engineering workflows; influence leaders on safe scaling patterns.

Responsibilities

• Own the Applied AI roadmap and turn business needs into platform capabilities.
• Scale agent workflows for modeling, pipeline creation, data quality, and classification.
• Build a secure integration layer 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 for outputs to be validated and traceable.
• Deliver across multiple workstreams and communicate risks and progress to senior leaders.
• Hire and grow a high-performing, inclusive engineering team.
• Sets direction and governance for agentic AI-enabled SDLC automation with guardrails for validation, security, resiliency, and reuse.
• Applies knowledge of SDLC toolchain, including AI-assisted development, to improve automation and capacity unlocks across teams.

Skills

Databricks

Unity Catalog

Delta Lake

Spark

LLM architecture

RAG

Orchestration

Production AI

Data governance

Immuta

Security

Resiliency

Business value

Tools

Databricks

Unity Catalog

Delta Lake

Sigma Computing

Job description

You will own the strategy and drive the technical direction for AI-enabled workflows that accelerate data modeling, pipeline generation, data classification, and governed self-service access, partnering closely with Information Architecture, Data Engineering, Data Governance, and business stakeholders.

As an Executive Director, Applied AI Engineering (Data Platform) at JPMorganChase within Corporate Technology, you will lead the design, delivery, and scaling of production-grade AI agent capabilities across our Databricks-based data platform on Amazon Web Services.

Job Responsibilities

• Own the Applied AI roadmap and turn business needs into platform capabilities.
• Scale agent workflows for 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.
• Sets direction and governance for agentic AI-enabled engineering and Software Development Life Cycle automation within a technical area to drive measurable improvements in speed, quality, and operational outcomes (e.g., release readiness controls, automated test modernization, 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.

Required Qualifications, Capabilities, and Skills

• Formal training or certification on software engineering concepts and 10+ years applied experience. In addition, 5+ years of experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise
• 10+ years demonstrated experience leading engineering teams and owning technical roadmaps for platform capabilities used by multiple product teams.
• Hands-on Databricks expertise (Unity Catalog, Delta Lake, Spark, SQL Warehouses) and ability to set secure, scalable patterns.
• Strong LLM application architecture skills (RAG, tools, agents, 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 (e.g., 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 (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to influence leaders on safe scaling patterns and reuse.

Preferred Qualifications, Capabilities, and Skills

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