Senior Machine Learning Engineer

Key Responsibilities:

• Design and implement scalable, production-grade Machine Learning systems in cloud environments.
• Architect end-to-end ML solutions from data ingestion to monitoring, managing containerized workloads with Docker and Kubernetes.
• Lead experimentation strategies, mentor engineers, and establish engineering standards for ML initiatives.

Required Skills & Qualifications:

• 5+ years building, deploying, and scaling Machine Learning systems with deep expertise in Python, SQL, and PySpark.
• Hands-on experience with ML frameworks like Scikit-learn, PyTorch, TensorFlow, and XGBoost, plus MLflow or similar tools.
• Experience deploying ML in cloud environments such as AWS, Azure, GCP, or Databricks and strong knowledge of Docker and Kubernetes.

Preferred Qualifications:

• Experience with healthcare datasets, including claims, eligibility, pharmacy, or EHR data.
• Strong understanding of MLOps practices including CI/CD for ML and model versioning.
• Experience with Kubernetes-native ML platforms such as Kubeflow, KServe, or Airflow on Kubernetes.