Sr Director of Software Engineering - AI, LLM, MCP

Job Description

If you are seeking a transformative career with one of the world’s leading financial institutions, this is your opportunity.

As an Sr Director of Software Engineering at JPMorgan Chase within 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.

 

Required qualifications, capabilities, and skills

• Formal training or certification on large scale technology program 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.

   

• 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 ambiguity—roadmap definition, backlog, dependency management, and stakeholder alignment.
• Strong communicator with executive-level stakeholder management, able to translate between engineering depth and business outcomes.

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.