Sr Director of Software Engineering AI, LLM, MCP

Job Description

If you are seeking a transformative career with one of the worlds leading financial institutions this is your opportunity.

As an Sr Director of Software Engineering at JPMorgan Chasewithin the Asset and Wealth Management AI4Tech team you will drive the execution and operationalization of AI-powered solutions across the SDLC. Your focus will be on delivering robust secure and scalable engineering outcomes embedding AI into production workflows to optimize developer productivity quality and security. This senior leadership role requires deep expertise in software engineering AI/LLMs agentic development patterns (A2A MCP) and a strong track record in production delivery and operational excellence. You will influence outcomes across a highly matrixed organization ensuring successful adoption and continuous improvement of AI-enabled engineering practices.

Job Responsibilities

1) AI-Native SDLC & Agent Fabric Implementation

• Lead the execution of an AI-native SDLC model across architecture coding security testing release and observability phases.

• Operationalize agentic patterns and toolchains including LLM orchestration skills context engineering and MCP-based integrations.

  Ensure responsible AI practices in production: guardrails evaluation monitoring and auditable workflows.

2) Cross-CTO Collaboration & Engineering Delivery

• Partner with App Dev leaders and platform owners to identify high-impact use cases validate value and scale production adoption.
• Translate engineering workflows into AI-enabled production capabilities (assistive to autonomous) that materially reduce developer toil.
• Drive alignment with ESP/GT/LOB stakeholders on control design security approvals platform standards and rollout approach.

3) Production Strategy & Portfolio Execution

• Define and execute the AI4Tech production vision multi-year strategy and delivery roadmap aligned to AWM CTO priorities.

• Establish measurable outcomes and operating mechanisms focused on developer productivity SDLC quality and vulnerability reduction.

  Own production governance: prioritization investment trade-offs risk posture and dependency management across CTO towers.

4) Adoption Metrics and Continuous Improvement

• Define and track production success metrics (examples): cycle time reduction PR throughput defect escape rate test coverage vulnerability density mean time to fix developer satisfaction and usage analytics.

• Create enablement assets (playbooks patterns best practices) to drive adoption across engineering teams and ensure consistent production outcomes.

• Sets and scales multi-department strategy for agentic AI-enabled engineering and SDLC/TLM automation (using enterprise-authorized tools within the work environment) to drive firmwide objectives (speed scalability reliability and cost-to-serve) including portfolio-level standards for AI-orchestrated delivery workflows release governance automated test modernization resilience engineering and incident response acceleration; establishes guardrails for validation security resiliency traceability and reuse.

• Applies knowledge of tools within the Software Development Life Cycle toolchain including enterprise-authorized AI-assisted development and automation capabilities to drive cross-domain reuse and measurable capacity unlock outcomes across departments.

Required qualifications capabilities and skills

• Formal training or certification on large scale technology program concepts and 10 years applied addition 5 years of experience leading technologists to manage anticipate and solve complex technical items within your domain of expertise.

• Deep expertise in AI/LLMs and their application to software engineering workflows (coding design security testing release).
• Hands-on experience with agentic systems tool/skill orchestration and integration patterns (e.g. MCP A2A function/tool calling).
• Proven ability to lead cross-functional engineering delivery amid ambiguityroadmap definition backlog dependency management and stakeholder alignment.

• Strong communicator with executive-level stakeholder management able to translate between engineering depth and business outcomes.

• Experience leading multi-organization adoption of agentic AI-enabled engineering operating models (using enterprise-authorized tools within the work environment) including defining governance (human-in-the-loop decisioning quality gates) measurement frameworks and secure handling of sensitive inputs/outputs across teams.

• Deep understanding of responsible AI risk controls and resiliency/security expectations at scale with demonstrated ability to advise senior leaders on safe adoption portfolio governance and reuse-first strategies.

Preferred qualifications capabilities and skills

• Experience with cloud-native ecosystems and AWS services (e.g. EKS Glue S3 EventBridge Lambda Flink).
• Familiarity with modern data architectures and sharing patterns (e.g. Iceberg Snowflake zero-copy sharing) data contracts/entitlements and cost optimization.
• Experience with secure SDLC practices and developer security tooling (e.g. SAST/SCA/container scanning) and vulnerability management metrics.
• API-first production design and integration patterns; strong analytics and experimentation discipline.