Solution Architect, Energy
We are seeking a technically skilled professional to serve as a bridge between developers, customers, and internal engineering teams in the energy domain. In this role, you will provide hands-on technical leadership, helping organizations integrate AI-driven solutions into power grid and energy operations using cutting-edge accelerated computing platforms.
Key Responsibilities
- Collaborate with business development, sales, and industry leads to advance ecosystem adoption of AI technologies in the energy sector
- Engage directly with developers and enterprise customers to guide implementation of AI frameworks and software libraries
- Assess and optimize application performance by analyzing system architecture and identifying acceleration opportunities
- Lead technical workshops, deliver training sessions, and conduct live demonstrations of AI platforms and tools
- Translate field insights into actionable feedback for product, research, and engineering teams
- Support end-to-end development of AI solutions, from prototyping to deployment, with attention to data pipelines, models, compute infrastructure, and orchestration
Qualifications
Candidates should hold an advanced degree in Machine Learning, Computational Science, Physics, or a related technical field. A minimum of five years of experience in artificial intelligence, deep learning, or generative AI is required, along with industrial experience applying machine learning to power grid operations and software systems.
Proficiency in Python and C/C++ is essential, as is hands-on experience with AI frameworks such as PyTorch and TensorFlow. Familiarity with GPU-based distributed computing, containerization, numerical computing libraries, and version control (e.g., GitHub) is expected. You should also have a solid grasp of modular software design and the ability to communicate complex technical concepts effectively.
Preferred Experience
- Direct experience with NVIDIA software libraries and GPU acceleration
- Background in Kubernetes, distributed training, and large-scale inference workloads
- Experience evaluating and deploying PCIe-based accelerators (e.g., GPUs, FPGAs, DSPs) across the development lifecycle
- Knowledge of embedded domain-specific languages such as CUDA, OneAPI, OpenCL, or HDL
Work Environment
This is a hybrid role offering flexibility in work location. The environment values innovation, inclusion, and personal growth, with a culture built around enabling individuals to do their best work. We offer competitive compensation, comprehensive benefits, and a workplace committed to diversity and equal opportunity.
