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Hybrid, mid-level ML role with broad skills and popular title increases competition.
ML and MLOps skills are broadly transferable, though automotive/manufacturing experience is advantageous.
Explicit 6+ years plus mandatory ML, MLOps, cloud and deployment skills creates high shortlisting strictness.
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Design, develop, validate, and deploy scalable AI and Machine Learning models across manufacturing, operations, quality, supply chain, and enterprise functions to drive measurable business impact.
Partner with business stakeholders to identify high-value AI/ML opportunities and integrate AI solutions seamlessly into enterprise workflows and operational systems.
Monitor and maintain model performance, ensure adoption of AI solutions, and drive continuous improvement and innovation including MLOps practices and cloud-based deployments.
Bachelor’s or Master’s degree in Computer Science, Data Science, AI, Engineering, Mathematics, or related field.
6+ years of professional experience in Data Science, Machine Learning, Artificial Intelligence, or Advanced Analytics.
Strong programming skills in Python with experience in relevant ML libraries and SQL; experience with MLOps tools, cloud platforms (preferably Microsoft Azure), and deployment of production ML models.
Not explicitly mentioned in the JD: Notice period, specific location onsite requirements, or regulatory constraints.
Experienced in handling end-to-end AI/ML lifecycle including model development, deployment, monitoring, and scaling in enterprise environments with proven business impact.
Comfortable working with cross-functional teams including data engineering, IT, cyber security, and business stakeholders to embed AI solutions into operations.
Has advanced knowledge of machine learning algorithms, MLOps, cloud AI services, and modern data engineering concepts, along with strong communication skills to translate technical insights to non-technical audiences.