Build the platforms that make advanced AI practical at scale. In this role, you’ll shape standards, tooling, and reliable inference foundations that help engineering teams move faster with confidence. You’ll work hands-on with modern large language model serving stacks and performance tuning, while partnering closely with platform and product stakeholders. If you enjoy solving deep systems problems and enabling others through great developer experience, you’ll find meaningful impact and growth here.
Job Summary:
As a Senior Lead Software Engineer in Corporate Technology – AI, Machine Learning and Data Platform, you will lead the design and delivery of secure, stable, and scalable platform capabilities that simplify adoption and day-to-day use. You will set technical direction for tooling and runtime foundations, with a focus on production-grade large language model inference and Kubernetes-based deployment patterns. You will partner across engineering teams to improve reliability, developer experience, and operational outcomes through automation and standards. You will mentor engineers and reinforce inclusive, high-accountability ways of working.
Job Responsibilities
- Lead the design and delivery of platform standards and tooling such as command line interfaces, software development kits, libraries, templates, and automated checks to simplify adoption and day-to-day use
- Engineer and operate production large language model inference services using modern serving engines such as vLLM, TensorRT-LLM, SGLang, LLM-D, or equivalent systems
- 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