Semi Senior Machine Learning Engineer

Role overview

A remote-first role focused on taking machine learning solutions from prototype to production at scale. The engineer owns the end-to-end ML lifecycle—covering industrialization, deployment, monitoring, and reliability—and partners closely with data scientists, data engineers, and business stakeholders in a collaborative, fast-moving environment.

Responsibilities

- Industrialize, deploy, and scale machine learning models into production environments with operational excellence. - Design and maintain end-to-end training, inference, and retraining pipelines. - Build and manage CI/CD pipelines for ML workflows, including model tracking, versioning, and registry through MLflow. - Develop and expose scalable APIs for model serving and performance. - 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. - Promote modern MLOps best practices and ML architecture standards across the team.

Requirements

- Advanced proficiency in Python and SQL. - Hands-on experience with Spark or PySpark for large-scale data processing. - Solid experience building CI/CD pipelines and working with Git. - Working knowledge of MLflow, covering tracking, registry, and deployment. - Familiarity with Docker and Kubernetes concepts. - Practical experience with Azure Cloud services. - Understanding of model monitoring and observability practices. - Proven track record deploying models to production at scale.

Nice to have

- Hands-on experience with Databricks Workflows, Jobs, and Repos. - Familiarity with additional cloud providers such as AWS or GCP. - Production experience running Kubernetes workloads.

Benefits and work setup

- Fully remote working culture. - Full sponsorship of certifications across major cloud and data platforms. - Birthday off plus an additional vacation week. - Referral bonus program for helping grow the team. - Monthly benefits marketplace credits. - Annual team offsite trip.