AI Presales Consultant

Job SummaryWe are seeking a high-impact, strategic AI Presales Consultant to join our elite team. This is not a standard presales role. You will engage "upstream" with our most strategic clients, acting as their primary technical and strategic advisor on their end-to-end AI journey, from initial AI curiosity to a fully architected and scalable MLOps platform. You will design the "how" of their AI strategyYour mission is to position our entire full-stack AI portfolio, translating complex business challenges into fully architected solutions. You will be the expert who connects the business use case to the underlying supercomputing hardware, with a strong emphasis on our AI Platform. You will guide clients through the complexities of modern AI—from data pipelines and RAG architectures to model selection, inference optimization, and precise infrastructure sizing. If you are passionate about building the factory for AI, not just the product, this role is for you. What We Don't Expect (Focus of the Role)You are not expected to be a hardware specialist (e.g., designing server racks or comparing GPU silicon).You are not expected to be a domain-specific data scientist (e.g., building the final fraud detection model or NLP algorithm).Your focus is the platform that enables these two ends of the spectrum. Key ResponsibilitiesStrategic Client Advisory: Lead executive-level "Art of the Possible" workshops and technical discovery sessions to understand a client's business goals, data readiness, and AI maturity.Full-Stack Solution Architecture: Design holistic, end-to-end AI solutions that synergize our supercomputing hardware, AI software platform, and MLOps capabilities to meet specific client needs.Generative AI & LLM Expertise: Act as the subject matter expert on Generative AI. Architect and evangelize scalable data ingestion and preparation pipelines, specializing in Retrieval-Augmented Generation (RAG) frameworks.Infrastructure Sizing & Performance Modelling: Analyse customer workloads (data volume, model complexity, training frequency, inference throughput) to accurately size the required platform infrastructure, including Kubernetes clusters, data storage, and software licenses. This includes calculating compute, storage, and network requirements based on key performance metrics like model parameters, token performance (tokens/sec), desired latency, and concurrent user load.Model & Software Consultation:Advise clients on AI model selection, comparing the trade-offs of open-source vs. proprietary LLMs, fine-tuning vs. foundation models, and model quantization.Position and demonstrate our proprietary AI software platform, MLOps tools, and libraries, integrating them into the client's ecosystem.Inference Optimization: Design and architect robust, low-latency, and high-throughput inference solutions for complex AI models, including large-scale LLM serving.User Experience (UX) Advocacy: Collaborate with client teams to define the end-user experience, ensuring the solution delivers tangible business value and a seamless interface for data scientists, analysts, and application users.Sales Cycle Enablement: Own the technical narrative throughout the sales cycle. Build and deliver compelling presentations, custom demonstrations, and Proofs of Concept (PoCs). Lead the technical response to complex RFIs/RFPs. Required Skills & QualificationsExperience: 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 QualificationsDirect 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).