Sr Lead Software Engineer - AWS - Lead AI/ML Platform Engineer
JPMorgan Chase · Jersey City, United States +1 · 4d ago
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.
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
- Industry experience as a platform engineer, leading large-scale platform infrastructure delivery
- Deep AWS expertise covering networking (VPCs, DNS, cross-account connectivity, service mesh), scalability, multi-account architectures, and security
Strong hands-on proficiency in Golang or Python - Advanced Terraform and HCL skills including module design, state management, and multi-region, multi-account delivery
Production experience with Kubernetes / EKS at scale - Hands-on experience with AWS Sagemaker for model deployments and inference
- Strong DevOps background with complex CI/CD pipelines, infrastructure automation, and deployment strategies
- Self-directed: you make architectural decisions and drive to outcomes with minimal oversight
- 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
- Experience with LLM model benchmarking, evaluation, and performance optimization
- Comfort with ambiguity and greenfield architecture where no existing playbook applies
- Security expertise including threat modelling, secure infrastructure design, and compliance in cloud-native environments
- Industry-recognized certifications (e.g., AWS Solutions Architect, AWS DevOps Engineer)