AI Presales Architect

Cortex Consultants LLC; Mumbai; On-site; INR 2,000,000 - 3,000,000; Full time

Job summary

An innovative consulting firm in Mumbai is looking for a Senior AI Presales Consultant to provide strategic insights and technical guidance throughout the AI journey for clients. You will design holistic AI solutions and act as a trusted advisor, influencing clients' AI strategies. The ideal candidate has over 7 years of experience in technical roles, particularly with generative AI and MLOps, and excels in communication and consultative skills. This full-time position offers an exciting opportunity to shape AI initiatives across various industries.

Qualifications

• 7+ years in a customer-facing technical role.
• Deep understanding of LLM architectures.
• Expertise in AI software stack and MLOps principles.

Responsibilities

• Lead executive-level workshops to understand client goals.
• Design end-to-end AI solutions for client needs.
• Advise on AI model selection and optimize inference solutions.

Skills

Customer-facing technical role experience

Generative AI expertise

AI Platform & MLOps knowledge

Consultative & communication skills

Education

Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related field

Tools

Kubernetes

Kubeflow

NVIDIA Triton

Job description

Overview

Senior AI Presales (Principal Consultant) (Full-Stack & Generative AI) – Location: Mumbai (only) • Type of Hire: Full-Time • Min Experience: 15+ yrs

We 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 strategy, translate business challenges into fully architected solutions, and connect the business use case to the underlying supercomputing hardware with emphasis on our AI Platform.

Your mission is to position our entire full-stack AI portfolio and guide clients through modern AI complexities—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.

Key Responsibilities

• Strategic 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 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 to size the platform infrastructure, including Kubernetes clusters, data storage, and software licenses, using metrics like model parameters, tokens/sec, latency, and concurrency.
• Model & Software Consultation: Advise on AI model selection, 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 within the client's ecosystem.
• Inference Optimization: Design robust, low-latency, 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 and ensure tangible business value 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 PoCs. Lead the technical response to RFIs/RFPs.

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.

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. Ability to perform arithmetic sizing for GPU, CPU, memory, and network requirements.
• Fluently discuss performance metrics (tokens/second, latency, throughput) and their relationship to hardware choices (e.g., NVIDIA H100 vs. A100, memory bandwidth, NVLink/InfiniBand).
• AI Platform & MLOps: Expertise in AI software stack, MLOps principles (Kubeflow, MLflow), Kubernetes for AI workloads, and model serving platforms (NVIDIA Triton, KServe, or similar).
• Model Landscape Knowledge: Strong knowledge of AI models (e.g., Llama family, Mistral, GPT-family, foundation models) with ability to discuss fine-tuning, quantization, and pruning.
• Consultative & Communication Skills: Exceptional communication, whiteboarding, and presentation skills; ability to translate executive-level needs into detailed technical architecture and a compelling C-level value proposition.
• Education: Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related field.

Preferred Qualifications

• Direct experience with AI hardware providers or major cloud AI platform providers.
• Hands-on experience with parallel computing frameworks (CUDA, MPI).
• Experience in scientific computing, research, or HPC domains.
• Active contributor to the AI/ML community (e.g., publications, conference talks, open-source projects).