AI Engineer – High-Speed Data Pipelines & Grid Arbitration

Job Location: Reno Nevada

Company Overview:

Amperesand is disrupting industrial power with the first commercialized Solid State Transformer systems. Solid State Transformers systems are much more than a transformer replacement, enabling numerous advanced features to unlock solutions for data centers, EV charging, renewables, microgrids, and industrial installations. We are looking for mission driven team members passionate about making amazing products for worldwide electrification at maximum acceleration. Amperesand is building a global company and looking for talent across our geographies.

Responsibilities:

• Develop and implement machine learning models for grid arbitration, load balancing, and demand response to optimize grid operations and energy distribution.
• Design algorithms for predictive maintenance and anomaly detection using sensor data from Solid State Transformers (SSTs), energy meters, and grid devices.
• Use advanced techniques like reinforcement learning, deep learning, and supervised/unsupervised learning to enhance real-time decision-making and forecasting.
• Design and implement high-speed data pipelines capable of processing and analyzing massive volumes of real-time energy data from grid sensors, devices, and external sources.
• Work with tools like Apache Kafka, Apache Spark, Flink, and Hadoop to create scalable and low-latency data processing frameworks.
• Ensure the pipelines are optimized for high throughput and low latency to handle time-sensitive grid data and IoT sensor inputs.
• Develop AI-based grid arbitration systems to intelligently allocate resources, balance loads, and manage energy distribution based on real-time grid conditions and predictive models.
• Work on algorithms for dynamic pricing, peak load shifting, and fault tolerance in grid systems.
• Integrate AI systems with existing SCADA and grid management software to enable seamless automation and decision support.
• Engage with business and operations teams to understand real-world grid challenges and tailor AI solutions that drive real-world impact.
• Continuously monitor the performance of AI models and data pipelines, identifying areas for optimization, model retraining, and system improvements.
• Ensure models and pipelines perform well under real-world conditions, processing large-scale data without bottlenecks or latency issues.

Qualifications:

• 4 Years experience in software development and evidence to deliver highly robust production quality software products.
• Strong experience in machine learning, including supervised, unsupervised, and reinforcement learning techniques.
• Proficiency in deep learning frameworks such as TensorFlow, PyTorch, or Keras for building AI models.
• Familiarity with AI model deployment, model monitoring, and versioning.
• Hands-on experience with building real-time data pipelines using tools like Apache Kafka, Apache Spark, Apache Flink, or similar.
• Familiarity with distributed systems, microservices, and event-driven architectures.
• Strong skills in cloud platforms like AWS, Google Cloud, or Azure for deploying and scaling AI-driven applications.
• Experience working with big data technologies such as Hadoop, Apache Spark, or Flink for processing large datasets.
• Knowledge of SQL and NoSQL databases (e.g., PostgreSQL, MongoDB, Cassandra) for storing and querying large datasets.
• Proficiency in Python (including libraries like NumPy, Pandas, Scikit-Learn) for data manipulation and machine learning.
• Familiarity with Java or Go for backend system integration.
• Excellent written and verbal communications in English with all stakeholders
• Willingness and ability to travel up to 25% including internationally
• Entrepreneurial mindset with clear bias for informed action and leading new initiatives with limited resources and support
• Adaptability: Comfortable with rapid iteration, learning new technologies, and adapting to new challenges.
• Experience with smart grid technologies, grid management systems, or working with renewable energy data.
• Familiarity with grid communication protocols (e.g., IEC 61850, Modbus, DNP3) and integration with SCADA systems.
• Background in predictive maintenance and anomaly detection for energy systems or industrial IoT devices.