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Remote role and notable AI employer increase competition, but senior niche industrial ML reduces density.
Industrial physics-informed ML and asset-health expertise limit cross-industry transferability.
Explicit 12+ years plus industrial physics-informed ML, MLOps, cloud/GPU, and domain expertise create strict filters.
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Lead design and delivery of production-grade ML and physics-informed analytics for industrial use cases like anomaly detection, predictive maintenance, and asset health monitoring.
Own technical roadmap for DataOps pipelines handling high-volume industrial sensor and time-series data, ensuring scalable deployment in cloud and on-prem environments.
Provide technical leadership and mentorship; translate industrial domain requirements into scalable AI product features; represent technical roadmap to internal leadership and key customers.
12+ years experience in analytics/ML engineering combining data-driven and physics/first-principles modeling approaches.
Bachelor's or Master's degree in Engineering (Mechanical, Structural, Electrical, or related discipline); advanced degree from top-tier institute preferred.
Strong Python programming skills with production-grade software engineering experience, including testing, performance tuning, and deployment.
Experience deploying models on cloud or GPU infrastructure (e.g., GCP, DGX) and working with industrial datasets such as signal/vibration, image, or process sensor data.
Extensive industrial R&D domain experience in asset health monitoring, diagnostics, prognostics, or additive manufacturing analytics.
Hands-on expertise in building and deploying ML-driven agentic and contextual AI workflows reducing manual fault diagnosis time.
Proven technical leadership managing enterprise or global R&D projects, mentoring engineers, and setting technical standards for analytics engineering.