Platform engineer in MLOps and DataOps

Opplane helps organizations unlock the value of their data in a world increasingly shaped by artificial intelligence, connectivity, and regulation. We work with businesses across complex, data-intensive industries to turn fragmented or difficult-to-use data into trusted, actionable intelligence. Our work spans AI and data solutions for banking and fintech, data governance and modernization, and connectivity technologies. Drawing on years of industry experience at leading companies such as PayPal, Amazon and Xerox PARC, our team combines expertise across data, engineering, AI and business domains to help clients move from technical complexity to practical solutions that support better decisions, stronger governance and real business outcomes.

• Build and maintain CI/CD workflows for ML workloads, covering testing, versioning, packaging and promotion across environments.
• Establish and operate ML lifecycle capabilities, including experiment tracking, model registry, deployment, serving and rollback procedures (e.g. MLflow).
• Implement observability for ML workloads, covering pipeline health, model and data drift, performance, reliability and cost.
• Work with data scientists to turn notebooks and experiments into governed, repeatable and production-ready pipelines.
• Administer and evolve data platforms, such as Databricks and Cloudera, ensuring consistent configuration, automation and operational standards.
• Build the foundations for reliable data operations, enabling data workflows to be monitored, troubleshot, reproduced and promoted across environments.
• Support the underlying cloud infrastructure, access management, networking and storage, working alongside other platform engineers.
• Apply security and governance principles across data and ML assets, including least-privilege access, traceability and auditability.
• Monitor and improve platform reliability, performance and cost, maintaining architecture documentation and operational runbooks.
• Work hands-on across a diverse data and AI ecosystem, including Databricks, Cloudera, Domino Data Lab, Chalk AI and others.
• Own the journey from ML experimentation to production, ensuring models and data workflows remain reliable, observable and scalable.
• Solve real engineering challenges around automation, platform integration, reliability and making workloads repeatable across environments.

• 4+ years of experience in Platform Engineering, DevOps, MLOps, DataOps or related roles.
• Solid DevOps foundations, including Git-based CI/CD, automation and infrastructure management.
• Hands-on experience with ML lifecycle management, including experiment tracking, model registry, versioning, deployment and monitoring.
• Working knowledge of Python, particularly for automation, scripting and integrating platform tooling.
• Understanding of security and governance principles for data and ML platforms, including access control and least privilege.
• Fluent English, both written and spoken.
• You build solutions that are repeatable, observable and maintainable.
• You troubleshoot methodically and get to the root cause.
• You collaborate across data science and data engineering teams.
• You adapt quickly to new tools and technologies.
• Будет плюсом: Hands-on AWS experience, particularly IAM, networking, S3 and related services; Infrastructure as Code experience (Terraform, Terragrunt) and Kubernetes environments; Experience with Databricks (Asset Bundles, Workflows, Delta Live Tables) or Cloudera; Familiarity with ML lifecycle and platform tooling such as MLflow, Domino Data Lab or Chalk AI; Experience with CI/CD and deployment automation using GitLab or Harness; Exposure to observability tooling (CloudWatch, Prometheus/Grafana) and cloud cost optimisation; Experience with feature stores, real-time model serving or production ML monitoring; Relevant Databricks or AWS certifications

• Small team, big challenges, real ownership.
• You’ll have access to leadership, opportunities to contribute to technical decisions and room to influence how we approach problems and build our products.