This role has closed. Robusta Studio is no longer accepting applications for this position. The page is kept live for reference; the listing below reflects the role as it was advertised.

Robusta assists organizations in transitioning to a digital-first approach, crafting unforgettable experiences for their customers. We provide strategy, design, product, and technology services to prominent businesses and brands, utilizing our go-to-market expertise to facilitate seamless customer experiences and enhance conversion rates.We're looking for a Senior MLOps Engineer to lead the design, deployment, and scaling of machine learning systems in production. You'll work at the intersection of data science, software engineering, and infrastructure to ensure reliable, efficient, and scalable ML pipelines. This role is ideal for someone who thrives in building robust systems and enabling teams to move faster with high-quality ML workflows.ResponsibilitiesDesign, build, and maintain scalable ML pipelines for training, testing, and deploymentDeploy & maintain machine learning models and ensure their performance, reliability, and monitoringCollaborate with data scientists and engineers to streamline experimentation and deployment workflowsImplement CI/CD practices for ML systems (ML CI/CD)Manage and optimize cloud-based infrastructure for ML workloadsDevelop monitoring, logging, and alerting systems for model performance and data driftEnsure reproducibility, versioning, and governance of ML models and datasetsAdvocate for best practices in MLOps, DevOps, and software engineeringRequirements5+ years of experience in software engineering, DevOps, or MLOps rolesStrong programming skills in Python (and familiarity with Java/Go is a plus)Experience with ML frameworks such as TensorFlow, PyTorch, or similarHands-on experience with containerization and orchestration tools (Docker, Kubernetes)Experience with cloud platforms (AWS, GCP, or Azure)Familiarity with CI/CD tools (e.g., GitHub Actions, Jenkins, GitLab CI)Strong understanding of data pipelines, distributed systems, and API developmentExperience with monitoring tools and logging frameworks