Senior AI & ML Platform Engineer — Enterprise Enablement

JPMorgan Chase

Columbus (OH)

On-site

USD 140,000 - 190,000

Full time

3 days ago

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Job summary

JPMorganChase in Columbus, OH is seeking a Lead Software Engineer within Corporate Technology and the AI/Machine Learning & Automation Center of Excellence to drive scalable adoption of responsible AI and intelligent automation. You will define standards, reference architectures, governance, and promote reuse across federated delivery teams to improve speed, resiliency, and control adherence.

You will collaborate with engineering, product, and business partners to embed scalable, safe practices

Qualifications

Formal training or certification on software engineering concepts and 5+ years applied experience.
Experience influencing senior stakeholders across multiple teams in a large 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, NLP, and intelligent automation concepts, with emphasis on enablement over direct model ownership.
Solid grounding in modern engineering practices, including SDLC, 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 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.

Responsibilities

Drive enterprise alignment and adoption of AI/machine learning and 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.
Enable reuse at scale by promoting shared tooling, accelerators, and reusable capabilities that improve time-to-market and consistency.
Provide advisory support 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.
Drive adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes, while establishing consistent validation standards.