Solutions Architect
Posted 5 Oct 2026
Advertised as “Nvidia Solutions Architect”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 8+ yrs
- Management level
- Individual contributor
- Employment
- Hybrid
No salary listed for this role.
Nvidia Solutions Architect
Location: Wrocław / Katowice / Warsaw - 2 days in office / 3 days remote
Let us introduce you the job offer by EY GDS Poland – a member of the global integrated service delivery center network by EY.
The opportunity
Join a high-impact role shaping next-generation enterprise AI solutions powered by NVIDIA. You will lead architecture for complex agentic systems, multimodal AI, and real-time vision platforms, working with advanced tooling to transform business use cases into scalable, high-performance AI products deployed at scale.
Your key responsibilities
Design and implement end-to-end AI architectures leveraging NVIDIA NIM, NeMo, DeepStream, and TensorRT; build multi-agent and RAG systems; optimize inference performance; define GPU infrastructure; and collaborate cross-functionally to deliver robust, scalable, and production-grade AI solutions.
Skills and attributes for success
Strong architectural thinking combined with deep hands-on expertise, ability to translate business needs into complex AI systems, excellent stakeholder communication, problem-solving in distributed GPU environments, and a pragmatic mindset focused on scalability, performance, reliability, and measurable business value.
To qualify for the role, you must have
- 8+ years of experience in infrastructure and software engineering, including 2–3 years with NVIDIA technologies.
- Hands-on expertise with NVIDIA NIM, NeMo Agent Core, NeMo Guardrails, NeMo Retriever, TensorRT and TensorRT-LLM.
- Experience designing multi-agent architectures, agent orchestration patterns and advanced RAG solutions using vector databases.
- Strong Python, C++, Linux, PyTorch and OpenCV skills, including experience with modern CV models such as YOLO, SAM or VLMs.
- Experience with Kubernetes, Docker, NVIDIA GPU Operator, KServe, cloud platforms and GPU environments such as DGX or InfiniBand.
- Willingness to travel occasionally.
Ideally, you’ll also have
- Experience with Kafka, GStreamer, RTSP streaming architectures and DeepStream SDK for real-time video analytics.
- LLMOps/MLOps experience, including model lifecycle governance and scalable deployment practices.
- Knowledge of AI security, guardrails design, knowledge graphs and graph-based retrieval.
- Experience with multimodal AI platforms, GPU capacity planning and performance tuning.
- Experience working in regulated enterprise environments.