Summary:
We are seeking a Machine Learning Operations Engineer with strong experience in the healthcare industry and expertise in deploying and maintaining production-grade machine learning models. The ideal candidate will have a deep understanding of healthcare standards, regulations, and electronic health record (EHR) systems. This role requires technical proficiency in machine learning pipelines, cloud platforms, and compliance standards, as well as a proven ability to collaborate with cross-functional teams to drive innovation in healthcare AI solutions.
Responsibilities:
- ML Model Deployment & Optimization: Deploy, maintain, and scale production-grade machine learning models to ensure real-time inference, reliability, and scalability.
- Pipeline Development: Create and optimize end-to-end AI pipelines, including data ingestion, preprocessing, search, and retrieval processes.
- CI/CD Integration: Build and maintain CI/CD pipelines for machine learning models, automating testing, and deployment processes.
- Monitoring & Logging: Implement monitoring and logging solutions to track model performance, system health, and detect anomalies.
- Collaboration & Leadership: Lead engineering efforts for ML/GenAI model development, large language model (LLM) advancements, and deployment frameworks in alignment with business strategies. Collaborate with data scientists, data engineers, and DevOps teams to achieve project goals.
- Compliance & Security: Ensure all systems meet healthcare-related security and compliance standards, including data protection and privacy regulations.
- Documentation: Maintain clear and comprehensive documentation of ML Ops processes, workflows, and configurations.
Qualifications:
- Bachelor's degree in Computer Science, Artificial Intelligence, Informatics, or a closely related field. Master's degree is a plus.
- Minimum of 3 years of relevant experience as a Machine Learning Engineer.
- Proven expertise in deploying and maintaining machine learning models in production environments.
- Strong knowledge of healthcare industry standards, regulations, and systems, including experience integrating ML models with Electronic Health Records (EHR) systems.
- Proficiency in cloud platforms such as AWS, GCP, or Azure for building scalable ML infrastructures.
- Skilled in containerization technologies such as Docker and Kubernetes
- Strong understanding of security and compliance standards for machine learning systems.
- Experience with version control systems to track changes in ML models and associated code.
- Certifications in machine learning or related fields- a big plus