Senior AI Foundation Platform Engineer (GenAI/Cloud-Native)

JPMorgan Chase

Palo Alto (CA)

On-site

USD 157,000 - 215,000

Full time

14 days+

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Job summary

JPMorganChase in Jersey City, with roles extending to Palo Alto and other locations, seeks a Lead Software Engineer to build AI foundation services for GenAI and ML at enterprise scale. You will lead hands-on delivery of secure, cloud-native platform capabilities (Kubernetes/CI/CD/IaC) and collaborate with application teams on reusable integrations.

You will drive adoption of AI-assisted development and ensure secure, reliable, and scalable production software, contributing to standards and best

Qualifications

Formal training or certification on software engineering concepts and 5+ years applied experience.
Strong hands-on coding experience in one or more languages used for platform services, such as Python, Java, or Go, with experience delivering production-grade services or APIs.
Experience building shared services, reusable components, or platform capabilities consumed by multiple application or engineering teams.
Experience with infrastructure-as-code and cloud-native delivery practices, including tools such as Terraform, containers, Kubernetes, CI/CD pipelines, and automated deployment workflows.
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; experience coaching engineers on safe, compliant adoption within delivery practices.
Hands-on practical experience with system design, application development, automated testing, debugging, and operational stability for production software.
Experience implementing observability, logging, metrics, alerts, Service Level Objectives, incident response practices, and root-cause analysis for services in production.
Working knowledge of software application development and technical processes, with depth in one or more areas such as cloud platforms, artificial intelligence, machine learning platforms, distributed systems, or infrastructure engineering.
Ability to break down technical requirements into executable engineering tasks, manage dependencies, and deliver against milestones in partnership with product and application teams.
Strong written and verbal communication skills, with the ability to explain technical decisions, trade-offs, issues, and risks to engineering teams and stakeholders.

Responsibilities

Partners with Lines of Business application teams to implement AI Foundation Services capabilities that unblock GenAI/AI use cases, supporting delivery from technical design through build, launch, and early operational support.
Builds and enhances reusable platform services, APIs, SDKs, and libraries that standardize how application teams consume model hosting, inference, and AI/ML managed services.
Translates functional and non-functional application requirements into clear technical designs, engineering tasks, and delivery milestones with support from senior engineers and architects.
Develops secure, stable, and high-quality production code, and participates in code reviews, debugging, testing, and remediation of defects across AI Foundation Services components.
Creates and maintains reusable engineering assets such as reference implementations, runbooks, test harnesses, baseline configurations, and onboarding guides to accelerate adoption 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, while establishing consistent validation standards and promoting reuse of patterns across the team.
Applies knowledge of tools within the Software Development Life Cycle toolchain to improve the value realized by automation.
Designs and implements scalable software components using cloud-native practices and platform engineering standards.
Collaborates with cross-functional teams across product, architecture, security, infrastructure, and application development to resolve dependencies and deliver production-ready capabilities.
Contributes to technical methods, standards, documentation, and implementation patterns within AI Foundation Services to improve consistency, reliability, and reuse.
Communicates technical progress, risks, dependencies, and implementation options to engineering managers, product partners, and senior technical stakeholders.