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Mid-level title and Bangalore location increase applicant density, but niche industrial ML reduces competition.
Strong industrial domain expertise and first-principles modeling required reduces cross-industry transferability.
Requires 5+ years, industrial ML experience, and production deployment skills, making screening strict.
Develop and deploy advanced AI/ML models for predictive maintenance and industrial process optimization using hybrid modeling integrating first-principles and data-driven methods.
Handle complex time-series and high-frequency sensor data from industrial IoT/SCADA/DCS systems to build scalable, production-grade AI inference pipelines under edge computing constraints.
Collaborate with domain experts to design reasoning agent workflows for dynamic environments and deliver measurable business outcomes in real-world industrial settings.
Bachelor’s or higher in Chemical Engineering, Mechanical Engineering, Aerospace, Applied Physics with computational modeling focus, or Computer Science with significant heavy industry experience.
5+ years of experience developing and deploying ML models in Manufacturing, Energy, Oil & Gas, or Power sectors.
Proficiency with Python, TensorFlow/PyTorch, Scikit-learn, SQL, and cloud ML platforms such as AWS, Azure, or GCP.
Location: Bangalore, Karnataka (Hybrid work model).
Expertise at the interface of data science and physical sciences with ability to embed domain engineering knowledge into AI solutions.
Experience leading technical teams and mentoring data scientists while being a hands-on contributor in production deployment.
Strong experience working with industrial operational data and designing agentic AI workflows that reason and adapt in noisy, dynamic industrial environments.