ML Ops

Are you passionate about building scalable AI platforms that accelerate the future of biologics drug discovery? We are seeking a Machine Learning Operations (MLOps) Engineer to join our AI Drug Discovery team within the Biologics organization. In this role, you will partner with AI scientists, computational biologists, and data engineers to develop and operate robust machine learning infrastructure, enabling the deployment of cutting-edge AI models for antibody discovery, protein engineering, and next-generation biologics research.

Key Responsibilities:

• Build and maintain end-to-end MLOps platforms and automated ML pipelines
• Support model training, deployment, monitoring, and lifecycle management
• Develop cloud-native AI infrastructure using Docker, Kubernetes, and modern MLOps tools
• Ensure scalability, reproducibility, and reliability of AI workflows
• Collaborate with cross-functional teams to translate AI research into production solutions

Key Requirements:

• Bachelor's degree with 5+ years of relevant experience, or Master's degree with 3+ years of relevant experience in Computer Science, AI, Data Science, Bioinformatics, or related fields
• Strong programming skills in Python and hands-on experience with machine learning platforms
• Experience with MLOps technologies such as MLflow, Kubeflow, Airflow, or similar tools
• Proficiency with Docker, Kubernetes, CI/CD pipelines, and cloud environments (AWS, Azure, or GCP)
• Strong problem-solving skills and the ability to work effectively in a multidisciplinary environment
• Experience in life sciences, biotechnology, pharmaceutical R&D, or AI-driven drug discovery is a plus