Required Qualifications
• Experience: 3+ years of experience in MLOps, DevOps, or Software Engineering with a focus on machine learning systems.
• Programming: Expert proficiency in Python and solid experience with writing clean, production-level code.
• Cloud & Containerization: Strong experience with a major cloud provider (AWS, GCP, or Azure) and expert knowledge of containerization technologies (Docker, Kubernetes).
• MLOps Tools: Hands-on experience with MLOps frameworks and platforms (e.g., MLflow, Kubeflow, Sagemaker, TFX, or similar).
• Technical Foundation: Deep understanding of the machine learning lifecycle, from data prep and model training to deployment and monitoring.
Preferred Qualifications
• Experience working in the HealthTech or FinTech industries, particularly with highly regulated data.
• Experience designing and managing data pipelines (ETL/ELT) for ML features.
• Knowledge of data governance, security principles, and compliance requirements in healthcare (e.g., HIPAA).
• Experience optimizing models for latency and throughput (e.g., ONNX, model quantization).
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.