Offline Analytics & On-Premises ML Application Lead

As the Offline Analytics & On-Premises ML Application Lead, you will bridge the gap between heavy industrial data engineering and cutting-edge machine learning application development. You will own the architecture of the on-premises data infrastructure, optimize it for training complex AI/ML models, and build internal applications that enable engineers and data scientists to securely build, test, and deploy AI models. - On-Premises Data Infrastructure: Design, scale, and manage on-premises data clusters optimized to store and process terabytes of historical EV telemetry. - Offline AI/ML Application Development: Lead the development of internal software tools and web applications that expose machine learning models to business units for diagnostic reporting, fleet health simulations, and warranty forecasting. - Heavy Compute ML Pipelines: Architect and maintain local GPU-accelerated computing infrastructure for offline deep learning, battery cell chemistry modeling, and training resource-intensive neural networks. - Data Ingestion & Synchronization: Establish efficient, secure data pipelines to ingest massive offline datasets and selectively sync key parameters with cloud environments. - MLOps & Model Lifecycle: Implement on-premises MLOps frameworks to manage version control, training datasets, and artifact storage for offline-trained models. - Data Governance & Security: Enforce strict data governance, localization protocols, and access controls for sensitive intellectual property. - Experience: 6+ years of experience in data engineering, machine learning engineering, or software development, with at least 2+ years leading technical teams or complex architecture projects. - Infrastructure Expertise: Deep experience building and managing on-premises distributed storage and compute frameworks and containerized workflows. - AI/ML Application Tooling: Proven track record of building production-grade application backends and integrating them with ML frameworks. - Hardware & Compute Fluency: Strong understanding of bare-metal Linux administration, GPU optimization, and network-attached storage configurations. - EV & Signal Processing Knowledge: Experience handling multi-gigabyte unparsed vehicle logs and running offline signal processing/feature extraction algorithms. - Education: Bachelors or Masters degree in Computer Science, Data Engineering, Software Engineering, or a highly related technical field. --- No additional details of the company are present in the provided job description. As the Offline Analytics & On-Premises ML Application Lead, you will bridge the gap between heavy industrial data engineering and cutting-edge machine learning application development. You will own the architecture of the on-premises data infrastructure, optimize it for training complex AI/ML models, and build internal applications that enable engineers and data scientists to securely build, test, and deploy AI models. - On-Premises Data Infrastructure: Design, scale, and manage on-premises data clusters optimized to store and process terabytes of historical EV telemetry. - Offline AI/ML Application Development: Lead the development of internal software tools and web applications that expose machine learning models to business units for diagnostic reporting, fleet health simulations, and warranty forecasting. - Heavy Compute ML Pipelines: Architect and maintain local GPU-accelerated computing infrastructure for offline deep learning, battery cell chemistry modeling, and training resource-intensive neural networks. - Data Ingestion & Synchronization: Establish efficient, secure data pipelines to ingest massive offline datasets and selectively sync key parameters with cloud environments. - MLOps & Model Lifecycle: Implement on-premises MLOps frameworks to manage version control, training datasets, and artifact storage for offline-trained models. - Data Governance & Security: Enforce strict data governance, localization protocols, and access controls for sensitive intellectual property. - Experience: 6+ years of experience in data engineering, machine learning engineering, or software development, with at least 2+ years leading technical teams or complex architecture projects. - Infrastructure Expertise: Deep experience building and managing on-premises distributed storage and compute frameworks and containerized workflows. - AI/ML Application Tooling: Proven track record of building production-grade application backends and integrating them with ML frameworks. - Hardware & Compute Fluency: Strong understanding of bare-metal Linux administration, GPU optimization, and network-attached storage configurations. - EV & Signal Processing Knowledge: Experience handling multi-gigabyte unparsed vehicle logs and running offline signal processing/feature extraction algorithms. - Education: Bachelors or Masters degree in Computer Scienc