Senior Solutions / Pre-Sales Engineer – AI Platforms

Company DescriptionOpen Innovation AI is a global technology company that specializes in developing advanced solutions for managing AI workloads. Its flagship product, the Open Innovation Cluster Manager (OICM), orchestrates complex AI tasks efficiently across diverse infrastructures. The platform is hardware agnostic, optimizedfor various GPUs and accelerators hardware, and facilitates seamless integration and scalability for enterprise AI applications. Open Innovation AI focuses on optimizing and simplifying AI workload management and making AI technologies accessible to organizations of all sizes. With its innovative solutions, companies can reduce operational costs, accelerate time to value, and maximize their return on investment, ensuring that their AI strategies contribute directly to enhanced business outcomeRole OverviewThe Senior Solutions / Pre-Sales Engineer is the core technical authority behind customer opportunities. This role sits at the intersection of AI platform architecture, GPU infrastructure, MLOps, customer advisory, solution engineering, and commercial proposal development.You will work with Sales from the earliest customer conversations to understand the business problem, challenge vague requirements, define the technical need, shape a solution that is both technically sound and commercially viable and work with the delivery team to reduce the friction in delivery. You are expected to go deep into the architecture, size the environment, build the Bill of Materials, support RFP and RFI responses, customize demonstrations, and create the technical proposal material required to move opportunities forward.The solutions you work on span Open Innovation’s AI platform, Agentic Fabric, and MLOps environments, along with the infrastructure and services required to run them at scale. This includes GPU accelerated compute, GPU orchestration and scheduling, quota and resource management, high-speed networking fabrics, AI optimized storage, Kubernetes based platforms, air gapped deployments, inference-as-a-service, training-as-a-service, fine-tuning-as-a-service, GPU-as-a-service, agentic workflows, agentic RAG etc.You will help customers understand why the solution is needed, how it fits their business and technical objectives, what trade-offs exist, and what it will take to implement successfully. This includes explaining the business and technology case for AI platforms, not just the underlying infrastructure.This is a hands-on senior individual contributor role. You will be expected to influence customer decisions, defend architecture choices, demonstrate the product stack, customize technical walkthroughs for customer-specific use cases, and turn complex requirements into proposals, sizing models, BOMs, architecture inputs, and delivery handover material that Engineering and Delivery teams can execute against.Key ResponsibilitiesOwn the technical pre-sales cycle for customer opportunities, from early discovery through solution definition, proposal development, product demonstration, technical validation, and delivery handover.Lead technical discovery sessions with customers to understand business objectives, AI use cases, workloads, data flows, model requirements, user personas, infrastructure constraints, security requirements, operational expectations, and success criteria.Translate customer requirements into practical AI platform, MLOps, GPU infrastructure, and application-layer AI solutions across compute, GPU systems, networking, storage, Kubernetes, orchestration, model serving, training pipelines, fine-tuning workflows, AI agents, and supporting infrastructure.Work with delivery teams to design and size GPU infrastructure for AI, machine learning, HPC, model training, fine-tuning, inference, and MLOps workloads, including high-density GPU cluster environments.Develop solution architectures around Open Innovation Platforms such as OICM, Kubernetes, air-gapped deployments, MLOps platforms, secure enterprise environments, model lifecycle management, and production AI operations.Support the design of inference-as-a-service, training-as-a-service, fine-tuning-as-a-service, and GPU-as-a-service offerings, including service tiers, user experience, access patterns, operational boundaries, and commercial positioning.Architect high-speed data-center networking for AI infrastructure, including Ethernet fabrics up to 800GbE, RoCE-based GPU-to-GPU traffic, segmentation, resiliency, and integration with customer networks.Size and position enterprise and AI-optimized storage platforms such as Ever Pure, NetApp, or comparable technologies for model training, checkpointing, inference, data pipelines, and high-throughput AI workloads.Demonstrate the Open Innovation AI platform stack to customers, including platform capabilities, administrative workflows, developer workflows, model deployment, inference services, training workflows, fine-tuning workflows, monitoring, access control, and operational controls.Customize demos, technical walkthroughs, proof-of-value scenarios, and solution narratives around customer-specific use cases, industries, data environments, security constraints, and business outcomes.Advise customers on AI agents, agentic workflows, retrieval-augmented generation, agentic RAG, tool use, workflow orchestration, vector search, knowledge base design, document ingestion, evaluation, guardrails, and integration patterns with enterprise systems.Explain the practical differences between base model deployment, prompt engineering, RAG, agentic RAG, supervised fine-tuning, preference optimization, reinforcement learning from human feedback, and other model adaptation techniques.Support technical discussions around training and fine-tuning approaches such as SFT, RLHF, DPO, GRPO, LoRA / QLoRA, instruction tuning, preference tuning, evaluation pipelines, model benchmarking, and deployment trade-offs.Build technical proposals, sizing models, Bills of Materials, BoQs, solution diagrams, architecture narratives, implementation assumptions, service definitions, and scope boundaries.Prepare and contribute to RFP, RFI, and tender responses, ensuring the technical response is accurate, coherent, competitive, and aligned with customer requirements.Conduct technical presentations, whiteboarding sessions, product demonstrations, architecture walkthroughs, and executive-level solution briefings for technical and non-technical customer stakeholders.Explain the business and technology case for proposed solutions, including productivity impact, AI adoption value, performance, scalability, security, governance, operational model, implementation risk, and commercial trade-offs.Work closely with Sales to shape opportunities, qualify technical fit, identify risks, define solution scope, and support commercial positioning.Partner with Delivery Engineering, Platform Engineering, Operations, Product, and vendors to validate feasibility, implementation approach, supportability, sizing assumptions, roadmap alignment, and cost assumptions.Create reusable reference architectures, baseline BoQs, proposal templates, demo flows, sizing calculators, service descriptions, architecture patterns, and technical response material to improve speed and consistency across opportunities.Engage with technology partners and vendors to validate architectures, confirm specifications, obtain pricing inputs, evaluate product capabilities, and stay current on relevant AI platform, MLOps, GPU, storage, networking, and model-serving technologies.Travel to customer sites across the UAE for workshops, demonstrations, technical validation sessions, proof-of-value exercises, and executive-level solution discussions.Required Qualifications7–10 years of experience in pre-sales engineering, solutions engineering, AI platform architecture, MLOps architecture, infrastructure architecture, systems engineering, or a similar customer-facing technical role.Strong hands-on background across AI platforms, MLOps, Kubernetes, compute, networking, storage, virtualization, and data-center environments.Proven experience supporting customer-facing solution design, including technical discovery, solution sizing, proposal development, BOM / BoQ creation, RFP / RFI responses, demonstrations, and technical handover.Solid understanding of AI infrastructure, GPU-based systems, MLOps platforms, model lifecycle tooling, and the infrastructure and platform requirements behind modern AI and machine learning workloads.Strong practical knowledge of Kubernetes, including how compute, networking, storage, GPU resources, security, scheduling, namespaces, quotas, access control, observability, and platform services come together in a production cluster.Understanding of GPU orchestration, resource scheduling, quota management, multi-tenancy, workload isolation, and capacity planning for AI platforms.Familiarity with inference-as-a-service, training-as-a-service, fine-tuning-as-a-service, GPU-as-a-service, or similar internal AI platform service models.Experience or strong familiarity with AI agents, workflow automation, RAG flows, agentic RAG, vector databases, embeddings, retrieval pipelines, tool calling, evaluation, guardrails, and enterprise integration patterns.Exposure to model training and fine-tuning techniques such as supervised fine-tuning, RLHF, DPO, GRPO, LoRA / QLoRA, instruction tuning, preference optimization, and model evaluation.Ability to design and size solutions across platform, compute, storage, and networking domains rather than working only within one technology silo.Experience preparing technical responses for RFPs, RFIs, tenders, and formal customer proposals.Ability to produce clear technical documentation, including architecture diagrams, proposal content, sizing assumptions, solution descriptions, demo narratives, service definitions, and handover material.Strong customer-facing presence, with the ability to explain complex AI platform, infrastructure, and model lifecycle concepts clearly to architects, engineers, data scientists, executives, procurement teams, and non-technical stakeholders.Commercial awareness: ability to balance technical quality, platform capability, cost, delivery complexity, supportability, security, risk, and customer budget realities.Ability to work independently in complex sales cycles without waiting for detailed instruction.Preferred QualificationsExperience designing, deploying, or supporting AI platforms for enterprise, government, regulated, or sovereign AI environments.Experience designing or supporting GPU infrastructure for AI, machine learning, HPC, large-scale data workloads, model training, inference, and fine-tuning.Experience with GPU scheduling and orchestration technologies, Kubernetes GPU operators, workload queues, model-serving frameworks, or AI platform schedulers.Experience with high-speed Ethernet, InfiniBand, RoCE, data-center fabrics, or AI cluster networking.Experience with enterprise storage platforms such as Pure Storage, NetApp, VAST Data, DDN, Dell, HPE, or comparable technologies.Experience with air-gapped Kubernetes, secure cluster deployments, regulated environments, or sovereign AI infrastructure.Experience building or demonstrating AI agents, enterprise copilots, RAG applications, agentic workflows, document intelligence solutions, or AI automation workflows.Arabic language ability is an advantage but not required.What Success Looks Like in the First 90 DaysYou are actively involved in customer opportunities from the earliest technical discovery stage, not only after the solution has already been shaped.You have led or materially contributed to at least one customer-facing solution proposal, including discovery, technical positioning, demo preparation, sizing, BoQ / BOM development, and internal handover.You have demonstrated the Open Innovation AI platform stack to customers and customized at least one demo or technical walkthrough around a real customer use case.Sales teams rely on you to qualify technical fit, shape the architecture, explain the platform value, and build the business and technology case for customers.Delivery and Engineering teams can take your proposal material and understand what was sold, why it was designed that way, what assumptions were made, what platform capabilities are expected, and what risks need to be managed.You have created or improved reusable pre-sales assets such as reference architectures, sizing models, baseline BoQs, platform demo flows, RAG / agentic AI solution patterns, proposal templates, or technical response content.At least one customer views you as a credible technical advisor who can guide architecture, AI platform, and model lifecycle decisions rather than simply answer product questions.