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JP Morgan Chase

Runs a consumer bank and an investment bank

Senior Lead Software Engineer-AI Foundation Services

Applied Machine LearningUpdated on Nov 10
Full-time$175.8k - $260kIn-person

About the Job

Requirements

  • Formal training or certification on software engineering concepts and at least 5 years of applied experience.
  • Hands-on experience designing, building, testing, and operating production software systems, distributed services, or platform capabilities.
  • Practical experience with AI/ML platform capabilities, model serving, model hosting, data access patterns, platform integrations, or infrastructure services supporting AI/ML workloads.
  • Experience developing cloud-native applications or platform services using Kubernetes, containers, continuous integration and continuous delivery, infrastructure as code, and modern engineering practices.
  • Proficiency in one or more programming languages such as Python, Java, or Go, with demonstrated ability to deliver high-quality production code.
  • Experience translating business or application team requirements into technical designs, implementation tasks, delivery milestones, and operational support plans.
  • Working knowledge of performance engineering and production reliability practices, including load testing, capacity planning, monitoring, alerting, service-level objectives and indicators, incident response, and root-cause analysis.
  • Experience applying secure-by-design engineering practices, including access controls, secrets management, vulnerability remediation, and secure handling of sensitive data.
  • Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment, for example coding, code review, test acceleration, and 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 and outputs, and adherence to resiliency and security expectations; experience coaching senior engineers and leads on compliant usage patterns and controls.
  • Demonstrated ability to collaborate across product, application, infrastructure, and security teams to deliver complex technical outcomes.

Responsibilities

  • Design, build, integrate, and optimize AI Foundation Services infrastructure components for generative AI and traditional AI/ML platforms, focusing on production-quality delivery and hands-on engineering execution.
  • Partner with Lines of Business application teams to co-develop reusable AI/ML foundational service capabilities, managed service integrations, and platform adoption patterns.
  • Translate Line of Business application requirements into technical designs, implementation plans, and engineering deliverables that support successful launch and early operational readiness.
  • Help de-risk AI/ML platform delivery across performance, scale, reliability, and security by contributing to non-functional requirements, test plans, runbooks, observability, and production readiness reviews.
  • Build reusable engineering assets such as reference implementations, deployment templates, test harnesses, onboarding guides, and GPU/training/serving baselines for model hosting platforms.
  • Drive adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes, including AI-assisted code review and refactoring, test acceleration, release readiness, and incident/root-cause analysis; establish measurable validation standards for secure coding, peer review, and automated testing.
  • Apply knowledge of tools within the software development life cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
  • Implement durable, maintainable code solutions and production platform capabilities that can be reused by multiple application teams and extended by other engineers.
  • Collaborate with product, platform, security, infrastructure, and application teams to resolve complex technical issues and deliver AI Foundation Services capabilities aligned to business priorities.
  • Participate in technical design reviews, operational readiness reviews, incident analysis, and continuous improvement activities to improve service reliability, scalability, and developer experience.

Desired Qualifications

  • Experience building or integrating GPU-backed model hosting, inference, training, or batch processing platforms.
  • Experience with large language model and model serving patterns, including routing, autoscaling, model gateways, inference optimization, evaluation workflows, and guardrail integration.
  • Experience optimizing AI/ML workloads for latency, throughput, reliability, and cost using techniques such as profiling, batching, caching, concurrency tuning, and capacity modeling.
  • Experience creating reusable developer enablement assets such as golden paths, reference architectures, deployment templates, onboarding playbooks, automated test harnesses, and operational runbooks.

About the Company

JP Morgan Chase

Runs a consumer bank and an investment bank

JPMorgan Chase runs the largest bank in the United States, which serves households and small businesses as Chase and large companies, governments, and investors as J.P. Morgan. During the 2008 financial crisis it bought the failing investment bank Bear Stearns and lender Washington Mutual, and in 2023 it took over First Republic Bank after regulators seized it. Chase takes deposits and earns interest on credit cards, mortgages, and car loans, while the investment side earns fees for advising on mergers, raising money for companies, trading stocks and bonds, and managing wealth. Formed in a 2000 merger, JPMorgan Chase traces its roots to 1799 and is based in New York City.

Company size
10,001+
Company stage
IPO
Headquarters
New York City, New York
Founded
1799

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