Principal Software Engineer- Core AI Platform

JPMorgan Chase & Co.; Seattle (WA); On-site; USD 180,000 - 260,000; Full time

Job summary

JPMorgan Chase & Co. in Seattle seeks a Principal Engineer for the Core AI Infrastructure Platform to unify training and inference pipelines across hybrid-cloud and Neo-cloud environments.

You will architect scalable services, mentor engineers, and drive reliability, security, and cost discipline at scale. Lead the development of foundation services for model hosting and inference, collaborate with product teams, and ensure robust observability and incident response while delivering on ambitious

Qualifications

• 10+ years of software engineering experience.
• Production-grade Python services delivery experience.
• Strong understanding of responsible AI in engineering workflows.
• Experience designing scalable AI infrastructure and platforms.
• Hands-on system design, testing, debugging, and production stability.
• Proven observability, incident response, and SRE practices.
• Ability to break down requirements into executable tasks and milestones.
• Excellent written and verbal communication skills.

Responsibilities

• Architect and design scalable AI/ML platform solutions.
• Build reusable platform services, APIs, SDKs, and tooling for model hosting and inference.
• Partner with AI infra teams to deliver foundation services enabling AI use cases.
• Own non-functional requirements and tooling for observability, security, and cost optimization.
• Establish standards and reference architectures for reliability and automation.
• Partner with product and platform teams to meet service reliability targets.
• Participate in on-call rotations and address complex production issues.
• Mentor engineers to raise engineering quality, documentation, and rigor.

Skills

Python

Production-grade services

AI infrastructure

Observability

System design

Cloud platforms

Distributed systems

Leadership

Mentoring

Strong communication

Tools

AI-assisted tools

Job description

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.

Job responsibilities

• Architect and Design solutions to enhance the reliability and scalability of AI/ML platforms and applications to accommodate fast-growing demand
• Buildand 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 architectureteams 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 inon-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