Description

You will own the full lifecycle management of machine learning models, from design to production deployment and maintenance. You partner with data scientists and clinical operations to deploy, monitor, and scale AI solutions that improve patient care and operational excellence. Your work ensures seamless integration and automation of AI systems within existing infrastructure.

Responsibilities

• Deploy, monitor, and maintain production-grade machine learning models with real-time inference, scalability, and reliability.
• Develop and manage end-to-end scalable ML infrastructures using AWS, Azure, or GCP.
• Build and optimize MLOps pipelines for model versioning, testing, debugging, and performance monitoring.
• Implement automation with Terraform and CI/CD tools like GitHub Actions to streamline deployment processes.
• Collaborate with engineering and data teams to design robust deployment pipelines and improve ML workflows.

Required Skills

• 5+ years of experience in Machine Learning Engineering or MLOps.
• Deep understanding of coding, architecture, and deployment processes.
• Proficiency in Python, R, and SQL.
• Experience with containerization (Docker) and orchestration (Kubernetes).
• Strong expertise in AWS, Azure, or GCP cloud platforms.
• Hands-on experience with GitHub Actions for CI/CD.
• Proven track record with LLMs, NLP, and predictive modeling.
• Ability to articulate RAG frameworks and their applications with LLMs.
• Familiarity with healthcare data, EHR systems, and relevant regulations.

Preferred Skills

• Master’s degree in Computer Science, Engineering, or a related field.
• Experience with technical writing and documentation for AI/ML models.

Key Skills

Education

Any Graduate