Join JPMorganChase’s Chief Data & Analytics (AIML Data Platforms) team in Jersey City as a Lead Software Engineer building AI foundation services for GenAI and ML at enterprise scale. You’ll lead hands-on delivery of secure, reliable, cloud-native platform capabilities (Kubernetes/CI/CD/IaC) and partner with application teams to create reusable integrations, reference implementations, and onboarding assets.

As a Lead Software Engineer at JPMorganChase within the AIML Data Platforms – Chief Data and Analytics team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. In this role you will get to drive significant business impact through your capabilities and contributions and apply your deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.

Job 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 (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.
• Designs and implements scalable software components using appropriate software design patterns, cloud-native practices, and platform engineering standards
• Collaborates with cross-functional teams across product, architecture, security, infrastructure, and application development to resolve technical dependencies and deliver production-ready capabilities
• Contributes to technical methods, standards, documentation, and implementation patterns within AI Foundation Services, helping improve consistency, reliability, and reuse across delivery teams
• Communicates technical progress, risks, dependencies, and implementation options to engineering managers, product partners, and senior technical stakeholders 

Required qualifications, capabilities, and skills

• 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 (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
• 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 

Preferred qualifications, capabilities, and skills

• Experience supporting AI/ML or GenAI platform capabilities, including model hosting, inference services, model gateways, managed AI services, or developer-facing AI/ML infrastructure
• Experience with GPU-enabled platforms or AI workload optimization, including inference latency, throughput, batching, capacity planning, or cost/performance tuning
• Experience building reusable “golden path” assets such as templates, reference implementations, SDKs, automated tests, onboarding guides, and deployment patterns
• Familiarity with model serving patterns, rollout strategies, safety controls, authorization, rate limiting, policy enforcement, and evaluation hooks