Job Title:  Platform Architect Analytics (AI/ML)

Job Purpose

EnerSys is at the forefront of revolutionizing DC Fast Charging Infrastructures, Microgrids, and Industrial Battery Solutions through artificial intelligence. As a Platform Architect Analytics for AI/ML, you will play a critical role in designing and optimizing AI/ML platforms that support both cloud-based and edge applications for battery analytics, energy optimization, and predictive maintenance. Your work will ensure scalable, high-performance infrastructure to support our AI-driven solutions, enabling real-time decision-making and automation across our products and services.
This role is essential to shaping EnerSys’ AI roadmap, integrating MLOps best practices, and creating seamless AI/ML pipelines that empower data scientists, engineers, and stakeholders. You will collaborate with cross-functional teams to ensure that AI solutions can be effectively deployed, monitored, and scaled in both Azure cloud and embedded environments, driving efficiency and innovation across our energy solutions portfolio. 
You will work in close collaboration with the Software Engineering department, which is responsible for delivering large-scale software solutions for manufacturing products. The AI team operates within this department, and your contributions will directly impact the successful integration of AI/ML capabilities into EnerSys’ digital products and services.

Essential Duties and Responsibilities

• AI/ML infrastructure development: Architect scalable, high-performance AI/ML platforms leveraging Azure cloud services.
• Data engineering & pipeline optimization: Develop and optimize data ingestion, transformation, and processing pipelines in Azure to deliver reliable and scalable AI solutions.
• MLOps & model deployment: Implement CI/CD pipelines for ML models and design distributed AI/ML computing architectures for training and inference workloads.
• Cross-Functional collaboration: Work closely with AI/ML engineers, software developers, data scientists, software engineering teams, and business leaders to align AI infrastructure with company objectives.
• Technology strategy definition: Evaluate and implement cutting-edge Azure AI/ML tools and cloud services.
• Real-time AI/ML at the Edge: Optimize AI/ML models for deployment on Azure IoT Edge devices within battery management systems and fast EV chargers.

Minimum Qualifications

• MS or PhD in Computer Science, Data Science, AI/ML, or a related field.
• 5+ years of experience in designing and deploying scalable AI/ML architectures.
• Proficiency in Microsoft Azure with expertise in Azure Machine Learning, Azure Databricks, Azure DevOps, and Azure Kubernetes Service (AKS).
• Experience in MLOps: Automating ML model lifecycle management with Azure ML pipelines, MLflow, and Kubernetes-based deployments.
• Strong programming skills: Python, SQL, Terraform. Experience in working with AI/ML frameworks like TensorFlow, PyTorch, or Scikit-learn is a plus.
• Expertise in big data technologies within Azure (e.g., Azure Data Lake, Azure Cosmos DB, and Azure Event Hubs) for AI/ML workloads.
• Knowledge of real-time data processing and edge AI: Understanding of Azure IoT Hub, Azure IoT Edge, and AI on Edge devices.
• Experience with networking and distributed computing concepts.

Preffered Qualifications

• Knowledge and experience in IP communication (Websocket, MQTT, etc.) and minimum two of CAN, I2C, SPI, RS232/485. Familiarity with real-time AI/ML applications for predictive maintenance and energy optimization.
• Hands-on experience with containerized deployments using Docker and Azure Kubernetes Service (AKS).
• Understanding of reinforcement learning, optimization models, and deep learning architectures in production environments.
• Exposure to battery analytics, energy management systems, or IoT-based AI applications.
• Experience working within or closely with software engineering teams delivering large-scale digital solutions
• Experience in leveraging NVIDIA Triton Server for efficient model deployment on NVIDIA Jetson platforms