MLOps Engineer

At Scalepoint, we support insurers in their digital journey to automate and provide a much better customer experience. Our solutions are unique, and our claims management solution was recently appointed the world’s best Now, we're looking for an experienced MLOps Engineer to play a central role in operationalizing machine learning models and scaling our AI solutions.

If you’re passionate about taking machine learning models from concept to production in a fast-paced, dynamic environment, and you want to help digitalize the insurance industry, this is the perfect opportunity for you As an MLOps Engineer, you’ll have the primary responsibility of designing, building, and maintaining the ML environment, shaping it from the ground up. You’ll also work closely with our data scientist, data team, and product managers, and will help ensure that our ML models deliver high business value and exceptional quality. This role is ideal for someone who thrives working independently and can communicate effectively across teams.

At Scalepoint, there’s always room to develop and explore new ideas. We believe high ambitions drive success and value curiosity, teamwork, and technical excellence. Together with the team, you will be involved with:

• Building and Managing ML/data Pipelines: Design, build, and maintain end-to-end machine learning pipelines that support data ingestion, feature engineering, model training, and deployment in production.
• Model Deployment & Automation: Work closely with data scientists to deploy machine learning models in production, utilizing CI/CD processes and automating workflows to streamline deployment and updates.
• Monitoring & Optimization: Implement monitoring solutions to track model performance, manage model drift, and maintain accuracy and reliability over time. Ensure models run optimally by fine-tuning infrastructure and resources.
• Collaboration with Cross-functional Teams: Partner with data scientists, software engineers, and DevOps to align ML models with business needs and optimize system performance.

We imagine that you are a problem-solver with a solid background in machine learning, DevOps, or software engineering and some years of relevant experience. Here’s what we’re looking for:

• Proficiency in Python: for developing and managing machine learning workflows.
• DevOps Experience: Strong experience with Docker and Kubernetes for containerization and orchestration, especially as our infrastructure runs on a Kubernetes cluster.
• Pipeline and Workflow Orchestration: Familiarity with tools like Dagster for pipeline management and dbt for data transformations.
• CI/CD Expertise: Experience with CI/CD tools such as GitHub Actions for automated deployments and integration workflows.
• Monitoring & Logging: Familiarity with tools like Prometheus, Grafana, or ELK Stack to monitor and log ML model performance in production.
• Communication Skills: Excellent communication skills to coordinate across a distributed team and work with both technical and business stakeholders.
• Independence & Initiative: Ability to work independently, especially in building and taking ownership of the ML environment, with a readiness to collaborate as the team grows.
• Adaptability & Curiosity: Willingness to stay updated with emerging MLOps practices and new technologies in the field.

This role will be essential in ensuring our machine learning solutions at Scalepoint continue to deliver cutting-edge performance and high-quality customer experiences in a scalable and reliable way. If you are driven, collaborative, and eager to make a tangible impact in the insurance tech space, we’d love to hear from you