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Mid-level generalist ML role, metro location, and broad MLOps+model requirements drive high competition.
Core ML engineering skills are transferable, but healthcare compliance and domain knowledge raise sensitivity moderately.
Explicit 5-7 years plus many mandatory stacks (PyTorch/TensorFlow, MLOps, cloud, Docker/Kubernetes) increases strictness.
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Design, train, and optimize production-grade ML models using PyTorch or TensorFlow.
Build and maintain scalable data pipelines and ML workflows incorporating MLOps tools like Airflow, Kedro, and MLflow to automate model deployment and lifecycle management.
Collaborate with analytics and product teams to develop dashboards and visualizations, monitor model performance including drift detection, and ensure data quality and compliance.
5–7 years of experience in ML engineering or applied machine learning.
Proficiency in Python and ML libraries such as Pandas, Dask, NumPy, Scikit-learn, and experience with PyTorch or TensorFlow for model building.
Experience with MLOps tools (Airflow, Kedro, MLflow) and deploying ML models in production environments (APIs, batch, streaming).
Familiarity with distributed computing (Dask, Spark), containerization (Docker), orchestration (Kubernetes), and cloud platforms (Azure, AWS, or GCP).
Experienced ML engineer comfortable with end-to-end ML lifecycle management including feature engineering, model tuning, deployment, and monitoring.
Skilled in scalable data processing, ML model explainability, responsible AI practices, and building operational dashboards for stakeholder insights.
Able to work in agile, fast-paced environments with strong collaboration across data science and engineering teams, and practical expertise in MLOps and production ML systems.