• Deliver AI and ML-enabled applications, with deployed models ranging from classical ML models to natural language processing, protein language models, and large language models.
• Contribute to the development and maintenance of ML platform capabilities, including:
  • Data pipelines and feature engineering workflows
  • Model training, evaluation, and deployment pipelines
  • Experiment tracking and model registry systems
  • Model performance evaluations and monitoring
• Partner in implementation of AI and ML Ops best practices, including CI/CD, infra as code, monitoring, traceability and reproducibility
• Collaborate with cross-functional teams to transition standards and outcomes from experimentation to production-grade enterprise solutions
• Build and maintain scalable and efficient, productionized MLOps solutions on cloud platforms
• Develop and deliver training content and knowledge articles to educate resident scientists on model lifecycle management best practices

Required Skills:

• Software programming (python, version control, test driven development)
• Familiarity with cloud technologies (AWS preferred, Databricks)
• Hands-on experience with AI/ML model training and serving

Preferred qualifications:

• Experience with deployment and maintenance of AI/ML models in production
• Substantial experience with cloud technologies and platforms, such as Databricks, AWS (preferred), or Azure
• Experience with AI and MLOps frameworks and tools (mlflow, kubeflow, weights & biases, terraform, etc.)
• Experience with containerization (docker, Kubernetes),
• Familiarity with distributed data processing (e.g., spark)
  • Familiarity with data science libraries (scikit-learn, tensorflow, pytorch, langchain)