MLOps Engineer Senior

🚀 Join Our Data Products and Machine Learning Development Remote Startup! 🚀

Mutt Data is a dynamic startup committed to crafting innovative systems using cutting-edge Big Data and Machine Learning technologies.

We’re looking for a MLOps Engineer Senior to help take our expertise to the next level. If you consider yourself a data nerd like us, we’d love to connect! 🐶🚀

You'll be responsible for industrializing, deploying, monitoring, and scaling Machine Learning solutions in production, ensuring MLOps best practices, traceability, reliability, and operational excellence across the full model lifecycle. This role works closely with Data Scientists, Data Engineers, and business stakeholders, playing a key role in turning ML models into robust, production-grade systems. Strong technical ownership, attention to detail, and a passion for building reliable ML platforms are essential to succeed in this fast-paced, collaborative environment. 

•  Industrialize, deploy, and scale Machine Learning models into production environments.
• Design and maintain training, inference, and retraining pipelines end-to-end. 
•  Build and maintain CI/CD pipelines for ML workflows, ensuring smooth and reliable releases. Implement and manage model tracking, versioning, and registry using MLflow. 
• Develop and expose APIs for model serving, ensuring performance and scalability.
• Orchestrate workflows and jobs on Databricks (Workflows, Jobs, Repos). 
• Containerize ML applications with Docker and support deployment on Kubernetes-based infrastructure. Implement model governance and versioning practices to ensure traceability across the ML lifecycle. 
• Collaborate closely with Data Scientists, Data Engineers, and business stakeholders to align technical solutions with business needs. 
• Promote MLOps best practices and modern ML architecture across the team.

• Advanced Python and SQL. 
• Experience with Spark / PySpark. 
• Solid experience with CI/CD pipelines and Git. 
• Experience with MLflow (tracking, registry, and deployment). 
• Experience with Docker and working knowledge of Kubernetes concepts.
• Experience with Azure Cloud.
• Experience implementing model monitoring and observability practices.
• Strong understanding of MLOps and ML architecture principles.
• Experience deploying models to production at scale. 

• Hands-on experience with Databricks (Workflows, Jobs, Repos).
• Experience with other cloud providers (AWS, GCP)
• Experience with Kubernetes in production environments.