MLOps & Data Scientist

Our client, a top healthcare company, seeks a Machine Learning Operations & Data Scientist, which involves building and maintaining scalable machine learning pipelines, deploying predictive models into production, and ensuring the reliability and performance of AI-driven solutions. The ideal candidate will bridge the gap between data science and engineering, contributing to model development, automation, and cloud-based ML-Ops infrastructure. This is a contractual position through the end of 2026. The client prefers that the candidate to be nearby the greater San Francisco area, but they will consider all profiles from the Pacific and Mountain time zones. The Role. • Collaborate with data science teams to create a streamlined, automated pipeline for transitioning ML models from development to production. • Design, develop, and maintain processes for model versioning, training, deployment, and continuous updates, implementing best practices for monitoring and drift detection. • Design and build scalable, reproducible infrastructure for ML development and deployment using Infrastructure as Code (IaC) principles. • Integrate ML pipelines into continuous integration and continuous deployment (CI/CD) workflows. • Ensure reliable and efficient deployment of ML models and implement monitoring solutions to track model performance, data quality, and system health. • Implement robust security controls, access management, and ensure compliance with healthcare industry standards (e.g., HIPAA) during model deployment. • Lead, mentor, and foster the growth of a team of junior MLOps Engineers, providing technical guidance and cultivating a culture of continuous learning. • Work closely with business stakeholders, project managers, and cross-functional teams to understand objectives and communicate project progress effectively Requirements • Bachelor's degree in Computer Science, Data Science, Information Technology, or a related field. • Minimum of 4 years of professional experience in MLOps, machine learning, and DevOps. • 5+ years of hands-on experience in cloud engineering, infrastructure, or related roles. • Proficiency in Python, SQL, and relevant ML libraries (e.g., TensorFlow, PyTorch). • Expertise in software development methodologies, such as Agile, DevOps, and CI/CD. • Familiarity with cloud platforms (e.g., GCP, AWS) and containerization technologies (Docker, Kubernetes). • Proven experience implementing security controls and ensuring compliance in regulated environments. • Excellent communication, stakeholder management, and team mentorship skills Preferred Qualifications: • Preferred certifications in cloud platforms (e.g., AWS, Azure, GCP) and MLOps