Lead Nvidia AI Consultant

About the role

In this role, you will combine deep hands-on engineering expertise in GPU inference, LLM deployment, and modern AI infrastructure with the consulting gravitas to lead client engagements, shape enterprise architecture decisions, and drive pre-sales. This is not a pure advisory role — you will own technical outcomes, drive AI adoption, and shape go-to-market solutions working closely with clients, Nvidia stakeholders, and internal engineering teams.

Responsibilities

• Own and lead AI engagements end-to-end — from discovery, assessment, and strategy definition through architecture design and production implementation, including clearly scoped implementation plans and delivery outcomes
• Translate complex business and operational challenges into AI use-case definitions, solution roadmaps, and corresponding reference architectures aligned with client scope and strategic objectives
• Design and validate production-grade AI architectures leveraging NVIDIA technologies including NeMo, NIM, Triton, Riva, DeepStream, Metropolis, and Omniverse across cloud and on-premises environments
• Define reference architectures for GenAI and Agentic AI solutions, advising on GPU-accelerated infrastructure design, Kubernetes-based workload orchestration, and MIG/vGPU partitioning strategies
• Profile and benchmark AI inference deployments to validate GPU utilization, memory footprint, and cost-efficiency against target SLAs — not just functional correctness
• Continuously tune deployment configurations — batch size, concurrency, tensor and pipeline parallelism, quantization level, and KV-cache settings — based on profiling data to hit optimal latency, throughput, and cost tradeoffs
• Benchmark LLM serving stacks across frameworks such as vLLM, TGI, and Triton+TensorRT-LLM, measuring throughput, TTFT, TPOT, and tokens/sec/GPU to drive informed infrastructure decisions
• Lead pre-sales activities including proposals, solution positioning, technical discovery sessions, and customer workshops, and drive GenAI proof-of-concept initiatives that demonstrate clear and measurable business value
• Contribute to NVIDIA alliance go-to-market strategy by shaping industry offerings, reusable accelerators, demos, and solution blueprints
• Drive thought leadership through whitepapers, technical blogs, conference presentations, and industry events while actively mentoring engineering and consulting teams on NVIDIA ecosystem technologies and modern AI/ML practices

Requirements

• 6+ years of experience in AI consulting, GenAI/Agentic AI development, Machine Learning, or Deep Learning with a demonstrable track record of leading client-facing engagements end-to-end
• Strong hands-on expertise in Generative AI, Agentic AI, multimodal AI, transformers, LLMs, and VLMs with practical experience across the full AI lifecycle including experimentation, fine-tuning, optimization, deployment, inference, and monitoring
• Hands-on experience with Python and modern AI/ML frameworks including PyTorch, TensorFlow, Pandas, NumPy, and Hugging Face
• Substantial hands-on experience with at least three NVIDIA ecosystem platforms including NeMo, NIM, Riva, Metropolis, Omniverse, Triton, DeepStream, or TensorRT-LLM
• Hands-on experience deploying AI/ML workloads on Kubernetes including Helm, Operators, GPU device plugins, NVIDIA GPU Operator, and MIG/vGPU partitioning, alongside practical experience with self-hosted inference stacks such as vLLM or Ollama
• Working knowledge of model quantization techniques and inference optimization with hands-on experience using GPU profiling tools including NVIDIA Nsight Systems, Nsight Compute, nvidia-smi, DCGM metrics, PyTorch Profiler, and vLLM/Triton metrics endpoints
• Demonstrated ability to read GPU utilization signals, diagnose compute-bound, memory-bound, or I/O-bound bottlenecks, and translate findings into actionable architecture or configuration changes
• Experience designing and deploying AI solutions in at least one major cloud environment — AWS, Azure, or GCP — with strong understanding of TensorRT, Triton Inference Server, CUDA, DeepStream, and ONNX
• Strong advisory and stakeholder management capabilities with experience presenting to executive and leadership audiences and leading pre-sales engagements including proposals, technical discovery sessions, workshops, and solution positioning
• Deep understanding of enterprise architecture, distributed systems, Big Data, SDLC, MLOps, and AI governance practices combined with strong communication, analytical, and problem-solving skills