Drive Kubernetes-based deployment patterns, scaling strategies, networking approaches, and troubleshooting practices to support reliable platform operations
Optimize inference performance by applying a strong understanding of GPU memory behavior, including key-value cache sizing, memory bandwidth trade-offs, and compute bottlenecks
Evaluate and apply inference-time quantization approaches, balancing latency, throughput, cost, and output quality for real-world workloads
Implement secure, high-quality production code and automation that strengthens resiliency, observability, and operational readiness
Establish and maintain architecture and design artifacts, ensuring constraints and non-functional requirements are enforced through implementation and automation
Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation
Required Qualifications, Capabilities, and Skills
Hands-on experience building standards and tooling such as command line interfaces, software development kits, libraries, templates, and automated checks to improve platform adoption
Deep, hands-on experience with large language model inference systems such as vLLM, TensorRT-LLM, SGLang, LLM-D, or equivalent production serving engines
Hands-on experience building and operating production services on public cloud platforms such as AWS
Ability to design, deploy, and troubleshoot cloud infrastructure components used by platform services (e.g., compute, storage, networking, identity and access) in AWS
Demonstrated Kubernetes expertise across deployments, scaling, networking, and troubleshooting
Working knowledge of GPU memory architecture, including key-value cache sizing and behavior, and performance trade-offs between memory bandwidth and compute bottlenecks
Understanding of inference-time quantization trade-offs and how they impact latency, throughput, and real-world serving behavior
Ability to produce architecture and design artifacts and translate them into secure, scalable implementations
Strong understanding of software development lifecycle practices, including continuous integration and delivery, resiliency, and security expectations
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices
Preferred Qualifications, Capabilities, and Skills
Experience building or operating shared platform capabilities used by multiple engineering teams
Familiarity with model lifecycle tooling and patterns for safe deployment, rollback, and monitoring of inference services
Experience designing SLOs, error budgets, and operational controls for high-throughput platform services
Familiarity with service mesh or advanced Kubernetes traffic management patterns for inference workloads
Experience improving developer experience through self-service workflows and clear engineering standards
