• Engage with our CSPs to root cause functional and performance issues in NCCL and CUDA libraries. • Analyze and improve multi-GPU workloads performance through profiling, benchmarking, and tuning. • Understand and solve NCCL and NVSHMEM data movement issues in multi-node clusters. • Understand and solve CUDA porting issues for customer workloads. • Apply datacenter-specific scheduling and topologies for optimal performance. • Debug and resolve complex issues related to GPU computation, memory, and transports. • Collaborate with customers to understand their workload integration specific challenges to NCCL and CUDA libraries and suggest tailored solutions aligned with the NVIDIA ecosystem. • Collaborate with AE, FAE, and solution architects to deliver integrated customer solutions and technical documentation. • Collaborate with internal teams to help customers use the latest advancements in CUDA and in NCCL.

• Performance optimization, analysis, and tuning of DL models in various domains like LLM, Multimodal and Generative AI • Scale performance of DL models across different architectures and types of NVIDIA accelerators • Contribute features and code to NVIDIA’s inference libraries, vLLM and SGLang, FlashInfer and LLM software solutions • Work with cross-collaborative teams across frameworks, NVIDIA libraries and inference optimization innovative solutions

• Focus on architecting system; discovering, designing, developing and maintaining SmithRx product features using a varied technology stack such as Golang, JavaScript/Typescript, Node, GraphQL, and SQL (amongst others), while leveraging full CI and CD to iterate quickly • Collaborate with stakeholders and technical leads to translate requirements into high-quality, AI-native scalable software, while providing scope and risk assessments through comprehensive technical design documentation. • Be the Stochastic/Deterministic Bridge: Build the "safety rails" around LLMs, creating deterministic software systems that effectively manage, validate, and wrap the stochastic nature of AI models to ensure 100% accuracy in healthcare data. • Continually apply and enforce software development best practices, design patterns, testing, automation, tools and technologies by adoption of emerging AI technologies like Claude-code, Copilot or aikes. • Leverage AWS technologies, like Lambda, Managed Kafka, EKS to handle various types of jobs and batch processing • Troubleshoot production issues by performing triages for issues that arise, assessing the impact, creating and executing a plan for short-term and long-term mitigation, and performing root cause analysis to prevent future occurrence of issues. • Foster a collaborative learning culture by mentoring junior engineers in both traditional backend best practices and emerging AI-native development patterns, while driving technical excellence through code reviews and the exploration of innovative technologies. • Work with senior leadership to turn technical vision into a tangible roadmap every quarter

• Work in a team with other smart AIS employees using cutting edge technologies to solve challenging business problems • Build elegant, scalable, extensible, cost-effective solutions with an eye towards innovation and agility • Utilize problem-solving and decision-making skills to understand client pain points and to self- troubleshoot as challenges arise • Collaborate with other members of the development team and project managers to deliver solutions that surpass client expectations while meeting deadlines and budgets • Work in an agile environment with participation in daily stand-ups/scrum • Design, write, test, troubleshoot, and document application code • Learn new technologies and be aware of industry standards, best practices, and trends