Job description

You’ll lead the next phase of AI-enabled delivery for a modern data platform—taking agentic workflows from strong early results to scaled, production-grade capabilities. This is a hands-on leadership opportunity to shape strategy, set technical direction, and deliver measurable outcomes that improve speed, quality, and governance across data product delivery. 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. 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. Job 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 that improve 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 mechanisms. • Build and run a human-in-the-loop review model where agent outputs are validated, versioned, traceable, and approved through defined quality gates before production use. • Lead delivery across multiple concurrent workstreams, aligning cross-functional partners, managing dependencies, and communicating progress and risks clearly to senior stakeholders. • Hire, develop, and retain a high-performing team of AI engineers, data engineers, and platform specialists, creating an inclusive culture that balances experimentation with engineering rigor. • 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 • 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, with the ability to set secure, scalable patterns. • Strong expertise in large language model application architecture, including retrieval-augmented generation (RAG), tool integration, agent design patterns, and multi-agent orchestration with an emphasis on reliability and safety. • Proven experience building, deploying, and operating production AI-enabled systems with strong engineering discipline (testing, observability, change management, and resiliency). • Strong understanding of data governance and access control, including policy enforcement platforms (e.g., Immuta or comparable technologies) and auditability expectations. • Proven 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 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 designing and building Model Context Protocol servers or comparable tool-layer architectures that connect AI agents to enterprise platforms securely. • Familiarity with modern data modeling approaches (e.g., 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. Applied AI Director

Requirements

Must-haves

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 that improve 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 mechanisms.
Build and run a human-in-the-loop review model where agent outputs are validated, versioned, traceable, and approved through defined quality gates before production use.
Lead delivery across multiple concurrent workstreams, aligning cross-functional partners, managing dependencies, and communicating progress and risks clearly to senior stakeholders.
Hire, develop, and retain a high-performing team of AI engineers, data engineers, and platform specialists, creating an inclusive culture that balances experimentation with engineering rigor.
Required Qualifications, Capabilities, and Skills
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, with the ability to set secure, scalable patterns.
Proven experience building, deploying, and operating production AI-enabled systems with strong engineering discipline (testing, observability, change management, and resiliency).
Strong understanding of data governance and access control, including policy enforcement platforms (e.g., Immuta or comparable technologies) and auditability expectations.
Proven ability to communicate complex technical approaches as clear business value for senior stakeholders, including outcomes, trade-offs, risks, and investment needs.
Experience designing and building Model Context Protocol servers or comparable tool-layer architectures that connect AI agents to enterprise platforms securely.
Familiarity with modern data modeling approaches (e.g., 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.
Applied AI Director

Nice-to-haves

Preferred Qualifications, Capabilities, and Skills

Application prep

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Keywords to mirror

aisql

Platform: oracle-cloud
Effort: medium