ML Ops Engineer

What We Do - The Data People Shaping Tomorrow Vivanti was founded as a platform for talented technologists who want to make a real impact for their clients. Our mission is to transform businesses and to create a smarter, more connected world through the power of data and technology. Together, we are making the future happen - today.

Life at Vivanti Our people are the lifeblood of our business. At our core - we want people who are deeply curious about technology, willing to have a go and have a voice to solve our customer's most complex business problems. We value breadth at Vivanti - we know that no business is the same. We believe the best technologists have a few deep skills, but love to explore new tools. This means, regardless of what curveballs get thrown at us, we'll always be able to solve the problem. We also love to have fun - even our senior folks are techies at heart and we want to have a great time as we go on this journey together. Data Consultants are responsible for designing, building, and maintaining the infrastructure and architecture that allows for the collection, storage, and analysis of data. They play a crucial role in ensuring that data is accessible, reliable, and secure for data scientists and analysts.

What You'll Do Build and maintain scalable ML pipelines using Vertex AI Pipelines and Terraform IaC for infrastructure automation. Develop CI / CD pipelines for model training, deployment, and monitoring using Cloud Build, Git, and Cloud Source Repositories. Package and deploy ML models using Docker containers on Google Kubernetes Engine (GKE) or Cloud Run. Design and configure logging, monitoring, and alerting (Cloud Monitoring, Cloud Logging) for models in production. Create reusable templates for experimentation, model serving endpoints, and pipeline orchestration. Automate integration with data sources such as BigQuery Implement governance gates across the ML lifecycle from experimentation to decommissioning. Collaborate with stakeholders to map responsibilities, define lifecycle stages, and embed governance best practices. Work closely with data scientists, data engineers, and business SMEs during agile sprints to ensure robust deployment of forecasting models. Conduct knowledge transfer sessions and contribute to internal training and user guides Stay updated on the latest cloud technologies

What We Are Looking For 5+ years in an MLOps, ML engineering, or data engineering role. Deep hands-on experience with Google Cloud Platform, especially Vertex AI, BigQuery, Cloud Storage, and Cloud Functions. Strong proficiency with Python, Terraform, CI / CD tools (e.g., GitHub Actions, Cloud Build), and containerization technologies (e.g., Docker, Kubernetes). Experience with ML model lifecycle management, experimentation tracking, and model monitoring. Familiarity with MLOps best practices, including reproducibility, automation, and model versioning.