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Tier-1 brand, mid-level generalist ML title, metro location, and broad toolset increase competition.
ML engineering skills transferable, but industrial IoT and transformer domain needs moderately reduce portability.
Explicit years, mandatory ML production experience, and specific Azure/tool requirements raise screening strictness.
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Develop, deploy, and scale an end-to-end analytical solution to monitor and optimize Vapor Phase Drying (VPD) process in transformer manufacturing.
Work across software development, machine learning, and data pipeline management with cross-functional experts in dashboard design and ML.
Ensure compliance with internal/external regulations and maintain codebase management using Git, Docker, and Azure Cloud services.
Bachelor’s degree in Computer Science, Data Science, AI/ML, or related discipline.
3-5 years of relevant work experience including 2-3 years in Machine Learning or Data Science roles.
Experience with Azure cloud platform and deployment of at least 2 end-to-end ML projects in production.
Strong Python programming skills including libraries: pandas, numpy, scikit-learn; familiarity with Azure ML services, Databricks, DevOps, Data Factory; knowledge of SQL/NoSQL databases and ETL workflows.
Experienced in working with industrial or IoT sensor data and Time Series Forecasting models (ARIMA, Prophet, etc.).
Able to collaborate effectively across R&D, IT, production, and business teams with strong technical communication skills.
Proven track record in operationalizing ML models and optimizing production processes within an industrial or energy sector environment.