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.