Customer Facing - Presales Specialist

Job title : Customer facing Presales Specialist

Job type : Permanent

Requirements

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).