NVIDIA

Solutions Engineer

• Lead the inference strategy for a portfolio of EMEA AI Natives customers, guiding engagements from initial proof of concept to production-scale deployments
• Identify inference challenges across customer deployments, including latency, efficiency, cost per token, memory utilization, and low-latency networking
• Architect and optimize high-performance inference pipelines using NVIDIA Dynamo, TensorRT-LLM, vLLM, SGLang, and other inference backends
• Improve GPU utilization and AI cluster efficiency
• Translate customer insights and deployment patterns into actionable product feedback
• Develop the roadmap for the NVIDIA stack, including Dynamo, TensorRT-LLM, and NIM
• Define the technical direction for AI inference across EMEA
• Align NVIDIA and customer organization stakeholders to influence strategic technology decisions for next-generation AI inference at scale

Senior Solutions Architect – Large Scale Neural Networks Inference

• Develop technical relationships with customers building multimodal AI systems for document intelligence, personalization, and image/video analysis, from architectural planning through production deployment.
• Guide customers on model training strategies across modalities, including layout-aware document encoders, user-item interaction models, and spatiotemporal video representations.
• Address vision content challenges such as image resolutions, vision encoder optimization for production latency constraints, efficient video frame sampling, and temporal reasoning.
• Represent customer needs to NVIDIA product teams and translate field insights into roadmap decisions across NeMo, TensorRT-LLM, Dynamo, and RAPIDS.
• Engage the developer community through hackathons, technical talks, demos, and reference blueprints.
• Translate modern AI technologies into accurate, production-ready solutions delivering measurable business value.

Senior Solutions Architect – Multimodal AI

• Build and manage strategic technical relationships with leading EMEA AI model builders developing large-scale foundation models
• Define software stacks and infrastructure for large-scale training and post-training workflows, including Reinforcement Learning
• Guide customers on distributed training strategies and efficient large-scale training and post-training recipes using PyTorch, Megatron-LM, or NeMo (RL/Gym)
• Help customers optimize training and fine-tuning efficiency at scale, including GPU utilization, communication overlap, and memory management
• Collaborate with NVIDIA product and research teams to present customer needs
• Craft NVIDIA product roadmap based on customer feedback
• Build or support hackathons, demos, and technical conferences to animate the developer community

Senior Solutions Architect – Large Scale AI Training

• Guide EMEA AI Natives customers in deploying and optimizing large-scale inference workloads on multi-node GPU clusters
• Architect efficient inference pipelines for dense and sparse/latent MoE models distributing workload among thousands of GPUs
• Improve inference efficiency across quantization, speculative decoding, disaggregated prefill/decode, KV cache management, and WideEP for large MoE deployments
• Collaborate with NVIDIA product teams, including Dynamo, TensorRT-LLM, and NIXL, to accelerate customer success
• Animate the AI inference developer community across EMEA through technical workshops, hackathons, and reference architectures
• Establish technical direction for scalable, high-performance AI inference across demanding production environments

Senior Solutions Architect – Large Scale AI Inference

• Guide EMEA AI Native companies building image, video, and multimodal generation products in training and deploying pipelines on NVIDIA infrastructure
• Provide technical guidance on diffusion model architectures, including DiT, UNet, and flow matching, and their deployment across single- and multi-GPU environments
• Optimize generation pipelines
• Guide customers through the visual content generation stack, including codec-aware preprocessing, temporal consistency, video token representation, and efficient long-video inference
• Identify vision-workload performance bottlenecks, including memory-bound diffusion steps, attention scaling with resolution, and multi-GPU communication patterns for video
• Translate customer insights into actionable product feedback for NVIDIA research and engineering teams
• Contribute to the EMEA developer community through technical demos, workshops, and reference demos showcasing NVIDIA's stack

Senior Solutions Architect – Diffusion AI Models

• Develop and demonstrate Physical AI solutions based on Hyperscalers and NVIDIA’s software and hardware technologies
• Work directly with key customers and Hyperscaler partners to understand challenges and provide solutions based on NVIDIA products
• Perform in-depth analysis and optimization for GPU-accelerated systems using NVIDIA software platforms
• Support optimization of training and inference pipelines
• Partner with Engineering, Product and Sales teams to understand developer challenges and plan suitable solutions
• Enable product feature development and growth through customer feedback and proof-of-concept evaluations
• Build industry expertise and contribute to integrating NVIDIA technology into Enterprise Computing architectures
• Drive relationships with technical executives and managers to evangelize accelerated computing and Generative AI
• Engage with developers, researchers, data scientists, IT managers and senior leaders

Solutions Architect, CSP GTM

Developer Relations

• Build relationships across the EMEA Smart Spaces and Local Government ISV ecosystem
• Foster synergies between customers and partner organizations
• Energize the ecosystem through training sessions on NVIDIA innovations, strategy, hardware, and software
• Advise account management, business development, and solution architect teams to create and support new markets and accelerate ISV partner scale
• Serve as technical advisor, problem solver, and champion for the developer ecosystem in Smart Spaces and Local Government
• Demonstrate solutions integrating the NVIDIA stack into developer products, platforms, and pipelines
• Advise on technical enablement resources, including sample code, guides, demonstration pipelines, and tools
• Guide partners and startups through onboarding and integration with NVIDIA programs
• Map, track, and monitor the developer ecosystem to identify growth opportunities and inform technology roadmaps
• Collaborate with solution architects, engineering, product management, and marketing to drive developer engagement and partner adoption
• Engage partner engineering teams, technical leaders, and decision-makers to identify goals and solve technical challenges
• Represent partner technical needs and feedback to NVIDIA product and engineering teams to influence product roadmaps

Senior Developer Relations Manager – Smart Spaces, Local Government

Cloud Engineer

• Design and implement new features to accelerate network and storage
• Work closely with open-source communities and participate in relevant working groups
• Work with different teams across NVIDIA
• Mentor team members, enabling them to deliver high-quality software
• Design, build, and deploy high-performance and scalable clouds based on NVIDIA ConnectX NICs and BlueField DPUs

Software Engineer, Cloud Platforms

Full-stack Engineer

• Contribute to the design, development, and testing of the object storage system
• Develop storage capabilities supporting NVIDIA research teams and other users
• Extend availability and reliability of deployments at scale, including 10k+ nodes and exabytes of data
• Analyze and improve system performance at all levels
• Automate storage infrastructure end-to-end, including provisioning, management, and monitoring
• Contribute upstream to open source storage codebases

Senior Software Engineer, Object Storage – DGX Cloud

• Own the technical strategy and roadmap for BlueField platform boot integration, from early firmware through Linux login
• Set the technical direction for platform boot integration in partnership with the architecture team, domain experts, product teams, and firmware partners
• Lead complex cross-layer technical issues and drive technical decisions on reliability, security, performance, recovery, and platform readiness
• Define the operating model for boot integration, including interface contracts, qualification criteria, release decisions, technical risk management, and escalation paths
• Identify and assess emerging technologies, development methods, and tools
• Drive adoption of technologies, methods, and tools that improve product capability, quality, engineering efficiency, or time to debug
• Help drive current product development and shape future technical direction across firmware, kernel, architecture, and product teams

Senior Software Engineer, BlueField platform

DevOps Engineer

• Develop and support continuous integration (CI/CD) processes and automation workflows for various hardware and software products
• Build and maintain infrastructure across bare-metal, virtualized, and containerized environments
• Collaborate with global software developers, verification teams, and performance analysts to streamline release efforts
• Drive continual improvements in automation infrastructure, internal cloud tools, and high-scale grid computing solutions
• Deploy new products and manage infrastructure, associated processes, and systems with software engineering and other teams

DevOps Engineer, DOCA

• Architect and build an agentic AIOps system that monitors GPU fleet health, aggregates and correlates telemetry streams, surfaces intelligent alerts, and orchestrates diagnostic workflows and corrective actions
• Research, evaluate, and prototype data storage strategies and data representations across diverse database technologies and modalities
• Design distributed systems for high-density telemetry ingestion, processing, and real-time analysis in large-scale AI clusters
• Instrument services with metrics, logs, and traces for rapid debugging and continuous performance improvement
• Build and own model-serving infrastructure, including packaging, versioning, deployment, and monitoring of AI models in SaaS and on-premises environments
• Contribute to core platform libraries and abstractions that accelerate development across the AIOps engineering team

Senior Software Engineer, AIOps

• Contribute to the design and development of diagnostic tools and frameworks using Python for NVIDIA’s networking platforms
• Assist in developing efficient and scalable software, optimizing performance for high-throughput systems and real-time monitoring
• Help develop features that connect hardware behavior with software-level diagnostics and analysis
• Work closely with hardware, firmware, and driver teams to solve real-world technical challenges
• Gain exposure to the full software development lifecycle, from understanding hardware specifications to deployment and validation

Software Engineer – Networking Platforms, Diagnostics Tools, Performance

Account Manager

• Elevate and prioritize the most promising NVIDIA DSX, AI Infrastructure, EDA, and semiconductor ecosystem providers based on their capacity to scale, attract capital, and drive sustained sales offtake
• Establish and execute a comprehensive NVIDIA DSX and semiconductor ecosystem go-to-market plan with local and global partners
• Lead GTM strategy and execution for the Physical AI ecosystem; monitor performance, identify gaps, and recommend data-driven optimizations
• Deliver incremental revenue and pipeline growth quarter over quarter, including structuring and closing complex business deals
• Partner with internal teams to design full-system solutions that enable the cloud business
• Provide strategic, product, and tactical feedback from headquarters
• Collaborate with product, sales, and partnerships teams on strategic priorities and execution
• Represent NVIDIA at strategic industry forums and drive participation in high-value ecosystem events
• Own event strategy, including evaluation, prioritization, and coverage planning for NVIDIA-led and third-party industry events
• Drive enterprise adoption of NVIDIA software and hardware platforms
• Define GTM strategy, sales enablement materials, and partner and field training
• Work with sales account teams to create and close pipeline opportunities
• Provide customer-requirement feedback to the product team

Account Manager – Semiconductor

• Support Tesla Motors in automated driving, cockpit applications and the automotive ecosystem
• Work with OEMs, manufacturers and ISVs in autonomous driving and cockpit electronics
• Evangelize NVIDIA’s deep learning-based approach
• Develop strategic relationships with customers’ R&D and procurement teams
• Identify opportunities and drive design wins
• Understand customers’ system architecture, existing solutions, development plans, competition status, product strengths and weaknesses, market position, and technical/commercial challenges
• Engage and steer Development, Marketing and Operations teams to provide best-in-class customer service and drive business growth
• Identify vehicle platforms and key personnel at Tier 1 customers for NVIDIA automotive product applications