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Mid-level ML role with generalist AI title and metro demand increases applicant competition.
Core ML/MLOps skills are transferable across industries, though healthcare domain knowledge moderately matters.
Explicit 5–7 years and many mandatory ML, MLOps, and deployment tool requirements raise filter strictness.
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Design, train, and deploy production-grade ML models using PyTorch or TensorFlow, including maintaining end-to-end ML pipelines and scalable data processing workflows.
Develop and maintain MLOps workflows with tools like Airflow, Kedro, and MLflow to ensure reproducibility, deployment automation, and lifecycle management across environments.
Collaborate with analytics and product teams to design dashboards and visualizations for actionable business insights and ensure compliance with data governance standards.
5–7 years of experience in ML engineering or applied machine learning.
Strong proficiency in Python and libraries such as Pandas, Dask, NumPy, and Scikit-learn.
Hands-on experience with PyTorch or TensorFlow and MLOps tools including Airflow, Kedro, MLflow or equivalents.
Experience deploying ML models in production environments including APIs, batch jobs, or streaming.
Experienced in managing large-scale datasets and distributed computing frameworks (Dask, Spark) with a focus on scalable, production-ready ML solutions.
Skilled in MLOps practices including experiment tracking (MLflow, Weights & Biases), model monitoring, retraining, and automation workflows.
Experienced with BI tools like Power BI for analytics visualization, familiar with cloud platforms (Azure, AWS, GCP), containerization (Docker), and orchestration (Kubernetes).