SR Director of Software Engineering - AI Engineering

JPMorgan Chase & Co.•Jersey City, NJ

16h•$232,750 - $325,000

About The Position

As Senior Director of Software Engineering at JPMorganChase within the Commercial and Investment Bank Payments Technology team, you will deliver the execution and operationalization of AI-powered solutions across the SDLC and PDLC. 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, and a strong track record in delivery. You will influence outcomes across a highly matrixed organization, ensuring successful adoption and continuous improvement of AI-enabled engineering practices. team, you lead multiple technical areas, manage the activities of multiple departments, and collaborate across technical domains. Your expertise is applied cross-functionally to drive the adoption and implementation of technical methods within various teams and aid the firm in remaining at the forefront of industry trends, best practices, and technological advances.

Requirements

Formal training or certification on software engineering concepts and 10+ years applied experience, including 5+ years 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.
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.
Demonstrated prior experience influencing across highly matrixed, complex organizations and delivering value at scale
Experience leading complex projects supporting system design, testing, and operational stability
Experience with hiring, developing, and recognizing talent
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.
Expertise in Computer Science, Computer Engineering, Mathematics, or a related technical field

Nice To Haves

Experience working at code level
Experience with on-prem , cloud-native ecosystems and AWS services (e.g., EKS, Glue, S3, etc.,)
Familiarity with modern data architectures and sharing patterns , data contracts/entitlements, and cost optimization.
Experience with secure SDLC practices and developer security tooling and vulnerability management metrics.
API-first production design and integration patterns; strong analytics and experimentation discipline.

Responsibilities

Lead the execution of an AI-native SDLC and PDLC 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.
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 GT/LOB stakeholders on control design, security approvals, platform standards, and rollout approach.
Leads multiple technology and process implementations across departments to achieve firmwide technology objectives
Directly manage multiple areas with strategic transactional focus
Interface with senior leaders, stakeholders, and executives, driving consensus across competing objectives
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.