Customer Facing - Presales Specialist

Job title: Customer facing Presales SpecialistJob type: PermanentRequirements- Required Skills & Qualifications Experience: 7+ years in a customer-facing technical role (e.g., Presales, Solutions Architecture, AI Specialist, or Technical Consulting), with a proven track record of designing large-scale AI, ML, or HPC solutions. - Generative AI Expertise: Deep, hands-on understanding of LLM architectures. Must be able to architect, explain, and build PoCs for RAG pipelines, including vector databases (e.g., Milvus, Pinecone, Chroma), embedding models, and data ingestion strategies. - Critical Sizing & Hardware Acumen: - Direct experience in sizing AI infrastructure. Must be able to perform "napkin math" and detailed calculations for GPU, CPU, memory, and network requirements. - Must be able to fluently discuss performance metrics (tokens/second, latency, throughput, TFLOPS) and their relationship to hardware choice (e.g., NVIDIA H100 vs. A100, memory bandwidth, interconnects like NVLink/InfiniBand). - AI Platform & MLOps: Expertise in the AI software stack. Strong understanding of MLOps principles (Kubeflow, MLflow), Kubernetes (K8s) for AI workloads, and model serving platforms (NVIDIA Triton, KServe, or similar). - Model Landscape Knowledge: Strong, current knowledge of the AI model landscape (e.g., Llama family, Mistral, GPT-family, foundation models). Ability to discuss fine-tuning techniques, quantization, and pruning. - Consultative & Communication Skills: Exceptional communication, whiteboarding, and presentation skills. Ability to translate executive-level business needs into detailed technical architecture and build a compelling C-level value proposition. - Education: Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related engineering field. - Preferred Qualifications Direct experience working for an AI hardware (GPU, CPU, Supercomputer) or major cloud AI platform provider. - Hands-on experience with parallel computing frameworks (CUDA, MPI). - Experience in scientific computing, research, or other HPC domains. - Active contributor to the AI/ML community (e.g., publications, conference talks, open-source projects).

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