Sr Lead Software Engineer - AWS - Lead AI/ML Platform Engineer

JPMorgan Chase & Co.•Jersey City, NJ

1d•$171,000 - $260,000

About The Position

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products. As a Senior Lead Software Engineer at JPMorganChase within the Firmwide AI/ML Deployment Platform team, you are an integral part of a globally distributed team — spanning Glasgow, London, New Jersey, and India — that works to architect, build, and own the infrastructure that makes model deployment work at scale. You'll operate with significant autonomy: owning technical direction, engaging directly with US-based clients, and making architectural decisions with real production consequences. We build the control plane, APIs, monitoring, and deployment infrastructure that internal teams depend on. The platform is always evolving — new regions, new failure modes, new scale requirements. If you like owning problems end-to-end, making hard tradeoffs, and shipping systems that other engineers build on top of, you'll fit in.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience
Proficiency in architecting software solutions at scale — you've designed systems that other teams depend on
Self-directed and autonomous: you drive to outcomes without waiting for direction
Strong client-facing communication skills, effective across time zones in a distributed team
Deep knowledge of AWS and cloud-based infrastructure
Track record building resilient, production-grade platform
Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls.

Nice To Haves

Comfort with ambiguity and greenfield architecture where no existing playbook applies
Production experience with Kubernetes / EKS at scale
Hands-on experience with AWS Sagemaker for model training and deployment
Strong Golang skills in the context of infrastructure or platform services
Deep understanding of networking — VPCs, DNS, cross-account connectivity
Practical experience with LLMs — deployment, inference, or integration
Track record delivering Terraform across multi-account, multi-region environments

Responsibilities

Drive architectural vision for platform components: control plane integration, multi-region deployment, and disaster recovery
Design and implement APIs for retraining, scheduling, endpoint deployment, and autoscaling
Build infrastructure for seamless integration across control plane and client accounts
Engage directly with US-based clients — requirements, strategic solutioning, and debugging
Make independent architectural decisions and own technical tradeoffs with minimal oversight
Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
Drives decisions that influence the product design, application functionality, and technical operations and processes
Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
Applies 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.