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Mid-level ML generalist in a metro location with broad skill requirements increases applicant competition.
Core ML engineering skills are transferable, but manufacturing and enterprise integration needs create moderate domain bias.
Mandatory 6+ years plus extensive ML, MLOps, cloud, and deployment requirements raise shortlisting strictness.
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Design, develop, validate, and deploy scalable AI and Machine Learning models for manufacturing, operations, quality, supply chain, and enterprise functions.
Partner with business stakeholders to identify high-value ML opportunities and translate complex challenges into actionable data science solutions.
Ensure production readiness of AI solutions including deployment, monitoring, lifecycle management, and integration with enterprise systems using MLOps and cloud platforms.
Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Engineering, Mathematics, or related field.
Minimum 6 years professional experience in Data Science, Machine Learning, Artificial Intelligence, or Advanced Analytics.
Proficiency in Python, ML libraries (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch), SQL, and experience with MLOps tools and cloud platforms (preferably Microsoft Azure).
Experience deploying ML models to production and integrating AI solutions with enterprise workflows.
Experienced AI/ML professional with strong expertise in both model development and operational deployment (MLOps), focusing on scalable, reusable business solutions rather than proofs of concept.
Comfortable collaborating across business and technical teams to bridge business objectives with AI technical execution and drive adoption.
Demonstrated capability in driving measurable business impact in manufacturing or operations domains through AI innovations and management of AI lifecycle and governance.