Senior Lead Software Engineer- Inference platform engineer

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 JPMorgan Chase within the Corporate Sector, Infrastructure Platforms team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications. Job Responsibilities Provide technical guidance aligned to business goals, collaborating with engineers, contractors, and vendors. Develop secure, high-quality production code; review, debug, and improve others’ code. Influence product design, application functionality, and technical operations through informed decisions. Champion firmwide SDLC frameworks, tools, and best practices. Promote a culture of diversity, Opportunity, inclusion, and respect. Architect and deploy secure, scalable cloud platforms optimized for AI/ML workloads. Partner with AI teams to translate compute needs into infrastructure requirements. Monitor, manage, and optimize cloud resources for performance and cost efficiency. Build CI/CD, automation, and infrastructure-as-code to streamline ML deployment and operations. 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 Hands-on experience in system design, application development, testing, and operational stability. Strong experience with Kubernetes and containerization (Docker), including cluster operations and production troubleshooting. Proficiency in at least one programming language, such as Python, Go, Java, or C#. Ability to independently tackle design and functionality problems with minimal oversight. Strong knowledge of cloud computing delivery models (IaaS, PaaS, SaaS) and deployment models (Public, Private, Hybrid Cloud). Foundational understanding of machine learning concepts, including transformer architecture, ML training, and inference. Experience with Infrastructure as Code. Deep understanding of cloud component architecture: Microservices, IaaS, Storage, Security, and routing/switching technologies. 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 Foundational understanding of NVIDIA GPU infrastructure software (e.g., DCGM, BCM, Dynamo Inference). Proficiency with observability tools like Prometheus and Grafana. Experience in ML Ops and related tooling, including MLflow. Experience building and scaling a high-performance LLM inference platform—leveraging vLLM and GPU/CPU serving optimization—to deliver low-latency, high-throughput, production-grade model serving.