The role
From JPMorgan Chase's own posting.
As a Lead Software Engineer at JPMorgan Chase within Infrastructure Platforms Applied Artificial Intelligence and Machine Learning (AI/ML), you will join a small, specialist team responsible for creating horizontal capabilities, including reusable APIs, libraries, reference architectures, and practical enablement that accelerates delivery across key products and platforms.
We are seeking a Lead Software Engineer to join a small, specialist team responsible for creating horizontal capabilities, including reusable APIs, libraries, reference architectures, and practical enablement that accelerates delivery across key products and platforms. This is a hands-on technical specialist role, with the opportunity to create new systems and capabilities that will accelerate our business.
The ideal candidate brings a strong foundation in software engineering and AI/ML, along with proven experience leading the development and production operation of AI-enabled systems in secure, enterprise environments. In this role, you will collaborate closely with engineering teams across our group to develop priority use cases, design and build services, and promote best practices for scalable, resilient, and secure AI adoption. You will also mentor engineers, contribute to firmwide standards and thought leadership, and help ensure the organization stays at the forefront of AI engineering advancements.
Your deep experience of developing and deploying software to production in enterprise environments will be key to enabling our AI strategy, combining practical expertise with AI/ML knowledge to ensure our systems are secure, compliant and performant.
Job responsibilities
Design and develop modular, maintainable AI systems and interfaces aligned to Infrastructure platform standards, optimizing for scalability, resiliency, and long-term operability.
Partner closely with Applied AI/ML, Data Science and Cybersecurity engineers to develop secure AI solutions for Infrastructure Platforms and our consumers.
Create secure and high-quality production code, and reviews and debugs code written by others
Evaluate AI solution approaches from engineering standpoint to improve overall design quality, code quality, and operational outcomes.
Influence peers and project decision makers to adopt leading-edge technologies where they create measurable value.
Drives team adoption of enterprise AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team
Contribute to the engineering community by advocating firmwide frameworks, tools, and practices for the AI-enabled System Development Life Cycle (SDLC).
Foster a team culture of opportunity, inclusion, and respect.
Required qualifications, capabilities, and skills
Significant hands-on system/software development experience, delivering production services in secure, regulated environments.
Demonstrated experience leading technical discussions (technical direction, design reviews, quality standards, and operational readiness) across a team or program.
Significant experience in at least one modern programming language: Java, Python.
Experience using AI coding assistants (GitHub Copilot, Claude Code etc) to complete software engineering tasks.
Solid understanding of building AI solutions using an LLM backed architecture.
Strong understanding of API design, microservices, and event-driven architectures.
Experience building and operating cloud-native services on AWS, including Bedrock.
Strong DevOps practices: CI/CD pipelines, containerization (Docker, ECS/EKS), and infrastructure-as-code (e.g., Terraform/CloudFormation).
Strong prompt engineering capability, including system prompts, few-shot prompting, tool/function calling, and structured outputs (e.g., JSON schemas).
Demonstrated experience driving secure and effective adoption of enterprise-approved AI-assisted engineering tools (coding, review, testing, troubleshooting) and setting team expectations for validating AI outputs for correctness, performance, and security.
Preferred qualifications, capabilities, and skills
Financial Services industry experience