Solutions Architect
Indexed description
What You’ll Be Doing
- Design and develop reusable GenAI models and systems to support applied AI and AI for Science workflows.
- Build, maintain, and optimize scalable training and inference pipelines that can be reused across multiple projects and research collaborations.
- Collaborate with internal and external research partners to translate real-world requirements into robust, production-ready AI solutions.
- Deploy, profile, and optimize AI models on NVIDIA GPU platforms, ensuring reliability, performance, and efficient resource usage.
- Integrate AI components into end-to-end workflows, including world model and multi-agent–based applications, and ensure smooth interaction with existing systems.
- Master’s degree or Ph.D. in Computer Science, Electrical Engineering, Applied Mathematics, Physics, or a related field.
- 2+ years of experience in machine learning or deep learning, with hands-on work in at least one of the following areas: speech recognition, NLP/LLM, computer vision, or multimodal models.
- Proficiency in Python and experience with deep learning frameworks such as PyTorch or TensorFlow for training and deploying models.
- Strong understanding of model development workflows, including data preprocessing, experiment design, evaluation, and performance optimization.
- Experience working with cross-functional or research teams on prototypes, proof-of-concept systems, or applied research projects.
- Strong communication skills and the ability to work independently and collaboratively in a fast-paced environment.
- Experience with agent-based systems in real applications, such as interactive agents, workflow assistants, or knowledge-intensive tools.
- Familiarity with AI for Science or scientific computing workflows, such as simulation data analysis, experiment automation, or large-scale data processing.
- Experience deploying or optimizing models on NVIDIA GPUs, using CUDA, TensorRT, or other GPU-accelerated libraries and tools.
- Proven problem-solving skills, ownership mindset, and a focus on building scalable and reusable components.
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