Director of Software Engineering - Agentic AI

JPMorganChase•Jersey City, NJ

About The Position

As an Executive Director, Applied AI Engineering (Data Platform) within JPMorganChase Corporate Technology, you will lead the design, delivery, and scaling of production-grade AI agent capabilities across a Databricks-based data platform on Amazon Web Services. You will own the Applied AI strategy and technical direction for workflows that accelerate data modeling, pipeline generation, data classification, and governed self-service access, partnering with Information Architecture, Data Engineering, Data Governance, and business stakeholders. This hands-on leadership role focuses on scaling agentic workflows from early results to production-grade capabilities that improve speed, quality, governance, and operational outcomes across data product delivery and the Software Development Life Cycle. You will establish standards and guardrails for secure, reliable, traceable, and reusable agent-enabled engineering; lead concurrent workstreams and senior-stakeholder communications; and build and develop a high-performing team of AI engineers, data engineers, and platform specialists.

Requirements

Formal training or certification in software engineering concepts and 10+ years of applied experience.
5+ years of experience leading technologists to manage, anticipate, and solve complex technical items within the relevant domain.
Demonstrated experience leading engineering teams and owning technical roadmaps for platform capabilities used by multiple product teams.
Deep hands-on expertise with Databricks, including Unity Catalog, Delta Lake, Apache Spark, and SQL Warehouses, and the ability to set secure, scalable patterns.
Strong expertise in large language model application architecture, including retrieval-augmented generation, tool integration, agent design patterns, and multi-agent orchestration, with emphasis on reliability and safety.
Proven experience building, deploying, and operating production AI-enabled systems with strong engineering discipline, including testing, observability, change management, and resiliency.
Strong understanding of data governance and access control, including policy enforcement platforms such as Immuta or comparable technologies, and auditability expectations.
Ability to communicate complex technical approaches as clear business value for senior stakeholders, including outcomes, trade-offs, risks, and investment needs.
Hands-on experience using enterprise-authorized AI-assisted software development tools in the work environment for activities 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 designing and building Model Context Protocol servers or comparable tool-layer architectures that securely connect AI agents to enterprise platforms.
Familiarity with modern data modeling approaches such as star schema and Medallion architecture, sufficient to validate and guide AI-generated artifacts.
Experience delivering AI capabilities in regulated industries where governance, auditability, and control requirements are central to design and operations.
Familiarity with Sigma Computing or comparable analytics tooling that operates on top of Databricks.
Experience establishing reusable agent patterns, shared semantic layers, and platform accelerators that scale across multiple teams.

Responsibilities

Define and own the Applied AI roadmap for the data platform, translating business needs into capabilities that reduce manual effort and accelerate data product delivery.
Architect and scale agent-driven workflows for data modeling, pipeline generation, data quality, and data classification, with clear production-readiness standards.
Design and oversee a secure tool-integration layer, including Model Context Protocol tooling where applicable, that exposes platform capabilities to agents with controlled access and strong auditability.
Establish technical standards for agent interaction with Databricks workspaces, Unity Catalog, Delta Lake tables, and policy controls to ensure consistent and secure execution.
Ensure performance, scalability, reliability, and operational readiness of AI-powered pipelines and tools, including monitoring, incident readiness, and continuous improvement.
Build and run a human-in-the-loop review model in which agent outputs are validated, versioned, traceable, and approved through defined quality gates before production use.
Lead delivery across multiple concurrent workstreams, align cross-functional partners, manage dependencies, and communicate progress and risks to senior stakeholders.
Hire, develop, and retain a high-performing team of AI engineers, data engineers, and platform specialists, fostering an inclusive culture that balances experimentation with engineering rigor.
Set direction and governance for agentic AI-enabled engineering and Software Development Life Cycle automation within a technical area to improve speed, quality, and operational outcomes.
Establish guardrails for validation, security, resiliency, traceability, and reuse across teams, including for release readiness controls, automated test modernization, and incident triage acceleration.
Apply knowledge of Software Development Life Cycle tools, including enterprise-authorized AI-assisted development and automation capabilities, to improve automation value and support capacity-unlock initiatives at scale.