Principal Software Engineer- Core AI Platform
JPMorgan Chase · Seattle, United States · 26d ago
Staff / Lead / PrincipalOn-siteMachine Learning & AI
Our Global Technology Infrastructure group is a team of innovators who love technology as much as you do. Together, you’ll use a disciplined, innovative and a business focused approach to develop a wide variety of high-quality products and solutions. You’ll work in a stable, resilient and secure operating environment where you—and the products you deliver—will thrive.
High Risk Roles (HRR) are sensitive roles within the technology organization that require high assurance of the integrity of staff by virtue of 1) sensitive cybersecurity and technology functions they perform within systems or 2) information they receive regarding sensitive cybersecurity or technology matters. Users in these roles are subject to enhanced pre-hire screening which includes both criminal and credit background checks (as allowed by law). The enhanced screening will need to be successfully completed prior to commencing employment or assignment.
If you are looking for a game-changing career, working for one of the world's leading financial institutions, you’ve come to the right place.
As a Principal Engineer at JPMorgan Chase on the Core AI Infrastructure Platform team, you will design and promote the shared ecosystem that unifies our training and inference pipelines across hybrid-cloud and Neo-cloud environments. If you thrive on solving deep infrastructure challenges, establishing resilient SRE standards, and building foundational tech stack that enables thousands of engineers to safely and efficiently deploy cutting-edge AI models into production, this is your opportunity to shape the future of AI infrastructure at scale.
As a Principal Engineer at JPMorgan Chase on the Core AI Infrastructure Platform team, you will design and promote the shared ecosystem that unifies our training and inference pipelines across hybrid-cloud and Neo-cloud environments. If you thrive on solving deep infrastructure challenges, establishing resilient SRE standards, and building foundational tech stack that enables thousands of engineers to safely and efficiently deploy cutting-edge AI models into production, this is your opportunity to shape the future of AI infrastructure at scale.
Job responsibilities
- Architect and Design solutions to enhance the reliability and scalability of AI/ML platforms and applications to accommodate fast-growing demand
- Build and enhance reusable platform services, APIs, SDKs, agents, skills, and libraries that standardize how application teams consume model hosting, inference, and AI/ML managed services
- Partner with AI infrastructure training, inference, and architecture teams to implement AI Foundation Services capabilities that unblock AI use cases, supporting delivery from technical design through build, launch, and early operational support
- Own and evolve non-functional requirements and build/enhance tooling for observability, resilience, security controls, infrastructure management, and cost optimization
- Establish and enforce standards and reference architectures for reliability, observability, automation, and operational readiness across services
- Partner with product and platform engineering teams to define and meet service reliability targets, including performance, availability, and recoverability
- Participate in on-call rotations, debug and resolve complex production issues; identify systemic gaps and drive durable remediation
- Mentor and guide engineers; raise the bar on engineering quality, documentation, and operational rigor
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 10+ years applied experience
- Strong hands-on coding experience in Python with experience delivering production-grade services
- 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, 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
- Proven skills in managing AI infrastructure on cloud platforms including deployment, scaling, monitoring, and optimizing machine learning workloads
- Experience building reusable "golden path" assets such as templates, reference implementations, SDKs, automated tests, onboarding guides, and deployment patterns
- Experience developing generative AI applications/AI agents and/or implementing AI-assisted operations with appropriate guardrails