Platform Engineer – MLOps

About the role

We are looking for a Platform Engineer with an MLOps focus to build and operate the platform capabilities that enable data science teams to develop, deploy, and manage machine learning workloads reliably and at scale. You'll work across ML lifecycle tooling, CI/CD, MLflow, Databricks and Unity Catalog, while also supporting the AWS infrastructure underpinning the Lakehouse. Some client engagements extend into hybrid or on-premises Kubernetes-based ML platforms (feature platforms such as Chalk, or notebook environments such as Domino) - prior exposure to these is a plus, not a requirement. The role combines platform engineering, automation, governance, observability, and collaboration with data science and data engineering teams. Infrastructure as Code (Terraform/Terragrunt) and hands-on AWS experience are valuable but not core requirements. The fundamentals are Databricks, MLOps, and Python. You'll report into the Data & Platform Engineering team.

Opplane specializes in providing advanced data-focused solutions for financial services, telecommunication, and reg-tech to accelerate their digital transformation journey. Opplane leadership team is comprised of Silicon Valley serial entrepreneurs and experienced executives. Its expertise comes from years of specific industry experience at some of the world’s top companies, such as PayPal, Xerox Parc, Amazon, Wells Fargo, SoFi in the areas of product management, data technology, data governance, data privacy, security, machine learning, and risk management. Why Opplane Global & Multicultural – Diverse perspectives, global collaboration (US, Portugal, India and Singapore offices) ⚡ Startup Energy – Fast-moving, impact-driven environment Ownership Mindset – Engineers own what they build Collaborative & Friendly – Open, curious, and supportive culture

Responsibilities

• MLOps & ML Lifecycle Enablement: Build and maintain CI/CD pipelines for ML workloads – testing, packaging, and promotion of models and pipelines (Databricks Asset Bundles, Git-based workflows).
• Own ML lifecycle tooling: experiment tracking, model registry, versioning, and promotion gates (MLflow or equivalent).
• Define development-to-production promotion procedures for models and pipelines, including validation gates, rollback, and an audit trail of changes.
• Implement model deployment and serving patterns – including batch inference and real-time endpoints – using Databricks Model Serving or equivalent technologies, with rollback options.
• Build and maintain observability for ML workloads, covering pipeline health, model/data drift, performance, latency, and cost.
• Partner with data scientists to productionize notebooks and experiments into governed, reliable, and repeatable pipelines.
• Databricks & Cloud Platform Support: Administer and evolve Databricks workspaces, Unity Catalog metastores, and catalogs (e.g. bronze/silver/gold), with proper access controls.
• Support the AWS infrastructure underpinning the Lakehouse, particularly IAM, networking, S3 and related platform services - working alongside platform engineers with deeper AWS expertise.
• Security, Reliability & Collaboration: Apply least-privilege access and Unity Catalog governance across data and ML assets.
• Monitor and tune Databricks jobs and compute for cost and performance.
• Maintain lightweight architecture documentation and runbooks for deployment, troubleshooting, and production support.
• Partner with data engineering and data science teams to define and maintain platform standards, automation, and documentation.
• Translate ML workload requirements into reliable, governed, and reusable platform capabilities.

Requirements

• 5+ years of relevant experience in platform, DevOps, MLOps, ML engineering, or related engineering roles.
• Hands-on experience with the ML lifecycle: experiment tracking, model registry, versioning, and deployment (MLflow or equivalent).
• Experience building CI/CD pipelines (Git-based workflows) for ML or data workloads.
• Working experience with