Bell Integration
NVIDIA Engineer
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
Role Summary
We are seeking an NVIDIA Engineer to design and build prototypes, proofs of concept, and production-ready accelerators for large enterprise and public-sector clients. The role requires hands-on experience with NVIDIA’s AI stack, agentic AI frameworks, and enterprise deployment practices, with the ability to translate client problems into working technical demonstrations and scalable solutions.
Key Responsibilities
- Design and build AI use cases and POCs for strategic clients.
- Prototype agentic workflows, copilots, and automation solutions.
- Develop and optimize inference pipelines for large language models and multimodal AI.
- Build secure, governed, enterprise-ready AI demos and pilots.
- Work closely with the AI Director, solution architects, and delivery teams to shape technical proposals.
- Create reusable accelerators, blueprints, and reference implementations.
- Present technical solutions to client stakeholders and senior leadership.
- Support R&D experiments, benchmarking, and platform evaluations.
- Contribute to deployment patterns for cloud, sovereign cloud, and hybrid environments.
- Ensure safe and responsible AI design with guardrails, evaluation, and observability.
Required NVIDIA Skills
Infrastructure Runtime & Inference Optimization
- TensorRT-LLM, TensorRT, Triton Inference Server.
- CUDA-X Data Science, including cuDF and RAPIDS.
Microservices & Agentic AI Frameworks
- NVIDIA NIM, NVIDIA AI Blueprints, NeMo framework, NVIDIA AgentIQ Toolkit, NeMo Guardrails and NVIDIA NeMo Customizer.
Core Foundational Language & Vision Models
- Nemotron models, including enterprise-grade reasoning and tool-calling use cases.
- Cosmos for multimodal and physical AI scenarios.
- Experience using foundation models in agentic or retrieval-based workflows.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Spatial Intelligence, Vision & Optimization
- cuOpt.
- NVIDIA Metropolis Microservices.
- Riva.
- NVIDIA ACE.
Physical AI, Robotics & Digital Twins
- Omniverse / USD Composer.
- Isaac GR00T.
- Edge and local inference deployment concepts for industrial or physical environments.
Technical Skills
- Strong Python development skills.
- Experience with LLM application development.
- API integration and workflow automation.
- RAG architecture and document intelligence.
- Containerization with Docker and Kubernetes.
- Familiarity with cloud platforms, especially Azure.
- Experience with Git, CI/CD, and production deployment.
- Ability to create demos, POCs, and technical artifacts quickly.
- Good understanding of data preparation, evaluation, and observability.
Domain Experience
- Government and public services.
- Financial services, including investment banking or hedge funds.
- Pharmaceuticals, life sciences, or healthcare.
- Enterprise operations, shared services, or regulated industries.
Personal Attributes
- Strong problem-solving ability.
- Comfortable working in ambiguity.
- Fast prototyping mindset.
- Client-focused and commercially aware.
- Able to balance experimentation with production discipline.
About the Environment
This role sits at the intersection of AI engineering, client advisory, and solution development. The engineer will help the business move from idea to demonstrable value by using NVIDIA’s enterprise AI stack to build practical, secure, and scalable AI solutions.
Must-have qualifications
- Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
- NVIDIA certification is mandatory. Candidates must hold at least one current NVIDIA credential relevant to generative AI, agentic AI, or AI infrastructure.
- Proven ability to build AI use cases, prototypes, and POCs for large enterprise clients.
- Strong experience using NVIDIA AI products and frameworks for production-grade solution development.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Person Specification:
- Strong leadership, organizational and problem-solving skills with the ability to lead delivery in ambiguous, fast-moving client environments.
- Comfortable balancing hands-on technical depth with senior stakeholder communication, facilitation and decision-making.
- Strong presentation, stakeholder management and client-facing delivery skills, with the credibility to work across engineering and business audiences.
- Strong Azure skills are required, including experience with Azure AI services, Azure OpenAI, Azure AI Foundry, Azure AI Search, Azure Machine Learning, Azure Functions, Azure App Service, Azure Container Apps or AKS, and core Azure data and integration services.
- Experience delivering production-grade AI and data solutions on Azure, including security, observability, performance tuning, deployment practices and operational support.
- Ability to prototype rapidly in Python and/or C#, work with APIs and SDKs, and translate business requirements into scalable Azure solution designs.
- Knowledge of responsible AI, governance, security and production monitoring practices for Azure AI workloads.
- Business analysis, solution design and forward deployment engineering capabilities, with a focus on measurable customer outcomes and adoption.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills
Location