• Lead bring-up, validation, and debugging of large-scale AI clusters, infrastructure, and end-to-end workloads, setting the standard for how the team operates. • Bring up, tune, and benchmark AI pre-training, post-training, and inference workloads using PyTorch, NeMo / Megatron, TensorRT-LLM, and adjacent NVIDIA AI software stacks. • Profile and optimize end-to-end workload performance across compute, memory, networking, and communication layers using tools such as Nsight Systems, NCCL tests, and custom microbenchmarks. • Analyze scaling efficiency for distributed LLM workloads using data, tensor, pipeline, and expert parallelism across modern GPU clusters, and translate findings into concrete tuning guidance. • Own root-cause analysis of complex failures — hangs, performance regressions, topology sensitivity in large distributed environments. • Define and build the resilience and failure-attribution stack: detecting, triaging, and attributing node, fabric, and workload failures across the cluster at scale. • Build repeatable benchmark suites, automation, acceptance criteria, and qualification workflows on new platforms. • Tune runtime settings, communication parameters, and deployment configurations in close partnership with framework, systems, and platform teams. • Deliver actionable, data-driven recommendations based on profiling, benchmark results, and cluster characterization. • Mentor engineers, drive technical standards, and act as a force multiplier across the broader performance and infrastructure organization.

• Design, develop, and deploy Generative AI solutions leveraging Large Language Models (LLMs) and multimodal AI technologies. • Build and maintain scalable AI applications using cloud platforms such as Azure, AWS, or GCP. • Develop and optimize Retrieval-Augmented Generation (RAG) architectures, vector databases, and knowledge retrieval systems. • Fine-tune, evaluate, and monitor foundation models to improve performance, accuracy, and reliability. • Implement prompt engineering strategies and AI orchestration frameworks to support business use cases. • Collaborate with software engineering, data science, DevOps, and security teams to integrate AI solutions into production environments. • Develop APIs, microservices, and AI-powered applications following software engineering best practices. • Ensure compliance with AI governance, security, privacy, and responsible AI standards. • Monitor AI workloads, model performance, and operational costs, recommending continuous improvements. • Stay current with emerging Generative AI technologies, frameworks, and industry trends.

• Design and deploy AI platforms that integrate with infrastructure tools • Develop AI-powered workflows to automate operational tasks • Build AI-driven automation for incident response and operational workflows • Implement AI-powered monitoring and anomaly detection capabilities • Create intelligent operational dashboards with actionable insights • Ensure AI platforms operate reliably in production environments • Develop AI solutions for cost optimization and predictive capacity planning

• Add AI functionality into existing Python, Node.js, and Ruby codebases • Build LLM-powered features: chat, summaries, classification, smart search, document Q&A • Design lightweight RAG pipelines using embeddings and vector search • Work with vector DBs (pgvector, Pinecone, Qdrant) • Implement safe, reliable LLM endpoints (OpenAI, Anthropic, Azure) • Work directly with clients to shape AI features and reduce manual effort • Advise clients when NOT to use AI and navigate trade-offs around latency, accuracy, and cost