Lead/Senior Domino Data Lab with Python

We are looking for a seasoned Lead/Senior Domino Data Lab Engineer with solid Python expertise to architect, build, and roll out scalable data science applications and machine learning solutions. In this position, you will use the Domino Data Lab platform to create production-ready models, partner with cross-functional teams, and champion best practices throughout the ML lifecycle.

• Architect, build, and roll out applications and machine learning models on the Domino Data Lab platform
• Create and maintain Python-based solutions using modern frameworks and libraries
• Build REST APIs to expose models and services for enterprise consumption
• Oversee Domino Projects, Workspaces, Jobs, and Environments to support data science workflows
• Apply MLOps practices covering model deployment, monitoring, and productionization
• Partner with data scientists, engineers, and stakeholders to translate business needs into technical solutions
• Connect applications with various databases and data sources
• Set up CI/CD pipelines and containerization strategies for reliable delivery
• Guide junior team members and promote software development best practices
• Maintain the reliability, scalability, and performance of deployed models and services

• 5-14 years of professional software/data engineering experience
• Expertise in Domino Data Lab, including Projects, Workspaces, Jobs, and Environments
• Proficiency in Python development and scripting
• Skills in Pandas, NumPy, and Scikit-learn or similar Python libraries
• Background in developing REST APIs with FastAPI, Flask, or Django
• Understanding of MLOps principles and the end-to-end ML model lifecycle
• Knowledge of Git, Docker, and CI/CD workflows
• Familiarity with cloud platforms such as AWS, Azure, or GCP
• Background in model deployment, monitoring, and productionization
• Capability to work with databases and data integration solutions
• Understanding of software development best practices

• Background in Node.js
• Familiarity with Kubernetes for orchestration
• Knowledge of MLOps or Machine Learning platform tooling
• Proficiency in Jupyter or similar Data Science environments
• Competency in enterprise application integration