Agentic AI Director of Software Engineering at JPMorgan Chase
Agentic AI Director of Software Engineering at JPMorgan Chase. Est. up to $250k. Apply with swipeandhired.
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.