Machine Learning Engineer

Avita Care Solutions is dedicated to promoting health equity through comprehensive pharmacy services and clinical care delivery. They are seeking a Machine Learning Engineer to design and implement scalable ML solutions that enhance patient outcomes and optimize pharmacy operations using healthcare and pharmacy data.

Responsibilities:

• Design, build, deploy, and maintain production-grade machine learning models and pipelines using healthcare and pharmacy data (claims, EHRs, prescription data, formulary data, etc.)
• Develop robust end-to-end ML systems, including data ingestion, feature engineering, model training, validation, deployment, monitoring, and retraining
• Productionize predictive models related to medication adherence, utilization forecasting, cost optimization, and patient outcomes
• Collaborate closely with data scientists, pharmacy experts, clinicians, and engineering teams to translate business and clinical requirements into scalable ML solutions
• Implement MLOps best practices, including CI/CD for ML, model versioning, experiment tracking, performance monitoring, and automated retraining
• Optimize model performance, reliability, and latency for batch and/or real-time inference use cases
• Ensure all ML systems comply with healthcare regulations (e.g., HIPAA) and internal data governance, security, and audit requirements
• Contribute to ML architecture decisions, tooling selection, and platform improvements within the Azure ecosystem
• Document ML systems and communicate technical designs and tradeoffs clearly to both technical and non-technical stakeholders

Requirements:

• Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, Engineering, or a related field; or equivalent practical experience
• 3 years + of experience building and deploying machine learning systems in healthcare or pharmacy domains
• Strong proficiency in Python for machine learning and software development
• Solid experience with SQL and working with large-scale relational and cloud-based data stores
• Hands-on experience implementing and operationalizing machine learning models in production environments
• Experience with Azure cloud services for ML workloads
• Familiarity with Databricks and distributed data processing frameworks
• Experience with ML lifecycle tools for experimentation, deployment, and monitoring
• Subject matter expert mindset with ability to work independently and work collaboratively to achieve high‑quality outcomes
• Demonstrates compassion in supporting patients, partners, and team members
• Strong interpersonal communication skills to collaborate effectively across cross functional teams
• Applies resourcefulness to solve challenges effectively