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Mid-level ML role, metro location, and broad MLOps/data skillset create high candidate competition.
Core ML and MLOps skills are transferable, but healthcare compliance adds domain specificity.
Explicit 5–7 years and many mandatory ML/MLOps tool requirements make filters strict.
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Design, train, and deploy production-grade machine learning models using PyTorch or TensorFlow, including model evaluation and tuning.
Develop and maintain scalable ML workflows and automated deployment pipelines employing MLOps tools like Airflow, Kedro, and MLflow.
Handle large-scale data engineering tasks for feature engineering and model training using frameworks like Pandas, Dask, Snowflake, and Databricks, while collaborating cross-functionally for ML solutions and analytics dashboards.
5–7 years of experience in machine learning engineering or applied machine learning.
Strong proficiency in Python and ML libraries including Pandas, Dask, NumPy, Scikit-learn; hands-on experience with PyTorch or TensorFlow.
Experience with MLOps tools such as Airflow, Kedro, MLflow and production deployment of ML models (APIs, batch, streaming).
Experience with Power BI or similar BI tools for analytics and visualization.
Experienced in end-to-end ML pipeline development including data preprocessing, model development, deployment, and monitoring with drift detection.
Familiar with distributed computing and cloud platforms (Azure, AWS, or GCP) for scalable ML workloads and containerization/orchestration (Docker, Kubernetes).
Capable of collaborating with analytics and product teams to deliver actionable AI solutions and documentation aligned with business goals.