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Mid-level ML role in a metro with broad GenAI and MLOps requirements attracts many qualified applicants.
ML and GenAI skills transfer well across industries, though automotive domain knowledge is beneficial.
Explicit 6–10 years requirement plus mandatory production ML, cloud, and MLOps skills increases filter strictness.
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Design, develop, deploy, and optimize production-grade machine learning and AI solutions including Generative AI for real-world business applications.
Own end-to-end data science lifecycle collaborating with business, analytics, engineering, and platform teams to translate complex needs into scalable and reliable models.
Mentor junior data scientists and lead best practices in analytical rigor, MLOps, and continuous model improvement.
6–10 years of hands-on experience in data science, machine learning, or AI engineering roles.
Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or related field.
Strong proficiency in Python (including libraries like pandas, NumPy, scikit-learn, PyTorch, TensorFlow) and SQL, experience with big data technologies (Spark, Hive, Presto, Databricks).
Experience with cloud platforms (Azure, AWS, or GCP) and familiarity with MLOps tools and orchestration frameworks (Airflow, Kubeflow).
Senior-level data science professional with demonstrated ability to deliver and scale production-grade AI/ML solutions in a collaborative cross-functional environment.
Strong expertise in advanced machine learning, deep learning, and Generative AI methods including model evaluation and prompt engineering for GenAI.
Experience mentoring teams and driving best practices around MLOps, model deployment, monitoring, and continuous optimization.