Sr Lead Software Engineer AWS Lead AIML Platform Engineer

Be an integral part of an agile team thats 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. Youll 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 youll fit in.

Job 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.

Required qualifications capabilities and skills

• Formal training or certification on software engineering concepts and 5 years applied experience
• Proficiency in architecting software solutions at scale youve 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.

Preferred qualifications capabilities and skills

• 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