Lead Software Engineer - AI & Machine Learning

You will influence outcomes across a federated delivery model by enabling reuse, aligning stakeholders, and embedding practical frameworks that improve delivery speed, resiliency, and control adherence.

As a Lead Software Engineer at JPMorganChase within Corporate Technology and the AI/Machine Learning & Automation Center of Excellence, you will drive scalable adoption of responsible AI and intelligent automation by defining standards, reference architectures, and governance that teams can implement consistently.

Job Responsibilities

- Drive enterprise alignment and adoption of AI/machine learning and intelligent automation by partnering with engineering, product, and business teams to embed consistent approaches into delivery roadmaps.
- Influence cross-functional stakeholders to align priorities across federated teams, reduce fragmentation, and drive adherence to Center of Excellence frameworks and standards.
- Define, maintain, and evolve scalable reference architectures, implementation patterns, and standards that accelerate adoption while remaining practical for delivery teams.
- Enable reuse at scale by promoting shared tooling, accelerators, and reusable capabilities that improve time-to-market and consistency.
- Provide advisory support and constructive challenge to delivery teams to strengthen design decisions, implementation quality, and operational readiness of AI-enabled solutions.
- Establish and reinforce Responsible AI governance across distributed teams, including practices for ethical AI use, bias mitigation, data quality, and lifecycle management.
- Promote secure, resilient, and scalable engineering practices aligned to enterprise architecture expectations across cloud, APIs, and integration patterns.
- Lead and contribute to a community of practice to uplift capability, share patterns, and drive continuous improvement across teams.
- Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
- 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.

Required Qualifications, Capabilities, and Skills

- Formal training or certification on software engineering concepts and 5+ years applied experience.
- Demonstrated ability to influence and align senior stakeholders across multiple teams in a large, complex organization without direct authority.
- Experience operating within an enterprise enablement function or center of excellence model, focused on standards, governance, and scaled adoption.
- Strong understanding of AI/machine learning, generative AI (large language models and small language models), natural language processing, and intelligent automation concepts, with emphasis on enablement over direct model ownership.
- Solid grounding in modern engineering practices, including software development life cycle, scalable system design, and operational resilience.
- Experience designing or governing cloud-based architectures leveraging APIs, microservices, and integration patterns at enterprise scale.
- Proven ability to define and embed Responsible AI governance across distributed teams, including bias mitigation, data quality, and lifecycle management practices.
- Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
- 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; experience coaching engineers on safe, compliant adoption within delivery practices.

Preferred Qualifications, Capabilities, and Skills

- Experience developing reference architectures and reusable patterns for AI-enabled automation across multiple product or platform domains.
- Experience building and sustaining communities of practice, including facilitation, knowledge sharing, and capability uplift programs.
- Deep familiarity with data and analytics practices for large-scale data pipelines, preprocessing, and performance optimization in production environments.
- Experience defining adoption metrics and lightweight governance mechanisms that improve standardization without slowing delivery.
- Strong executive communication skills, including storytelling, facilitation, and the ability to translate strategy into consumable standards and playbooks.