Principal AI/ML Engineer

Machine Learning & Analytics Development

• Architect and build machine learning and physics-model-based analytics systems for industrial use cases such as anomaly detection, root-cause analysis, predictive maintenance, and asset health monitoring.
• Lead the design and delivery of agentic AI and contextual AI workflows that combine domain knowledge with data-driven models for autonomous fault diagnosis and predictive automation.

DataOps & Model Deployment

• Own the technical roadmap for DataOps pipelines that ingest, process, and contextualize high-volume industrial sensor and time-series data (e.g., vibration, acoustic emission, SCADA/Historian data).
• Drive model deployment and MLOps practices across cloud and on-prem environments (e.g., GCP, GPU/DGX infrastructure), including performance tuning and code optimization.

Engineering & Mentor Leadership

• Mentor senior and mid-level engineers, setting technical standards for analytics engineering and acting as a technical authority across cross-functional project teams.
• Develop, tune, and productionize machine learning models for quality inspection, anomaly detection, and process optimization.

Collaboration & Technical Representation

• Partner with product management and customer-facing teams to translate industrial domain requirements (e.g., compressor, turbine, or asset diagnostics) into scalable AI product features.
  • Represent the technical roadmap in front of internal leadership and, where required, key customers or partners.