What you’ll be doing
Translate public-sector mission goals, data constraints and operational requirements into technical architectures for generative and agentic AI solutions.
Collaborate with partners to build and demonstrate working prototypes using Python and PyTorch. Then define practical paths from proof of concept to production deployment on NVIDIA accelerated computing platforms.
Build containerized deployment patterns using Kubernetes, including model serving, scaling, integration and lifecycle considerations for customer environments.
We lead hands-on discovery sessions, technical workshops and field trials with customer engineers and ecosystem partners, with clear success criteria and measurable technical outcomes.
Guide model adaptation and inference workflows for large language models, then solve performance, compatibility and deployment issues with partner and technical teams.
We create reusable notebooks, code samples, architecture guidance and technical presentations that connect customer requirements with implementable solutions.
Bring structured customer and partner feedback to NVIDIA product and engineering teams to inform product requirements and remove adoption barriers.
What we need to see
Master’s degree in Computer Science, Electrical Engineering, Computer Engineering, Applied Statistics, Physics, Mathematics or another relevant engineering field or equivalent experience.
5 or more years of meaningful experience in solutions architecture, technical consulting, software development or a closely related technical role.
Strong hands-on programming skills in Python and practical experience using PyTorch to build, test and debug machine learning workflows.
Proven work with generative or agentic AI, including domain adaptation or deployment of agent-based solutions.
Practical experience packaging and deploying AI applications with containers and Kubernetes.
Ability to turn sophisticated customer requirements into clear solution architectures, working prototypes, and ready-to-implement technical plans.
Clear written, verbal and technical presentation skills, including the ability to communicate through code, notebooks, architecture documents and workshops.
Proven teamwork across customer, engineering, research, product, sales and partner teams in an international environment.
Ways to stand out from the crowd
Development experience with NVIDIA GPUs, software libraries or high-performance computing systems.
Applied knowledge of continuous integration and delivery, AI model lifecycle management, and repeatable deployment pipelines for AI systems.
Technical depth in one or more areas such as multimodal AI, computer vision, edge deployment, digital twins or data analytics.
Consulting or delivery experience with government customers, sovereign technology programs or AI startups.
Professional communication skills in French and English; additional Western European languages are a plus.
Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/