Platform Engineer

• Design, build, and operate the infrastructure, tooling, and pipelines that make machine learning reliable at scale.
• Sit at the intersection of data engineering, DevOps, and applied ML - owning the platforms and systems that let data scientists and engineers move from experiment to production safely and repeatably.
• Power intelligent products and internal automation across the company, and help shape how the organisation adopts ML and AI responsibly.
• Build and maintain MLOps automation end-to-end: CI/CD for models and pipelines, environment management, artifact versioning (models, data, prompts, code), and release governance.
• Implement and operate model serving infrastructure: deployment patterns (blue/green, canary, shadow), endpoint management, scaling, and latency/throughput optimisation.
• Build and maintain training and experimentation infrastructure: job orchestration, compute provisioning, experiment tracking, hyperparameter management, and reproducibility tooling.
• Implement observability for ML systems: data quality checks, feature drift detection, model performance monitoring, bias checks, alerting, and incident response workflows.
• Build and maintain data pipelines for ingestion, transformation, feature engineering, and export across multiple sources and destinations.
• Design and maintain a feature store or feature platform layer: serving consistency, point-in-time correctness, and reuse across teams.
• Expose well-governed datasets, features, and APIs that models, pipelines, and downstream consumers can rely on.
• Enforce secure data handling and compliance with relevant data protection standards, access controls, and audit requirements.
• Contribute to documentation, platform standards, and continuous improvement of ML engineering processes across teams.

ML Platform Engineer

• Design and develop Model-Driven Apps leveraging Dataverse tables and relationships
• Architect and maintain Dataverse data models
• Build, optimize, and maintain Power Automate flows
• Implement business process automation with Business Process Flows
• Develop Canvas Apps for custom UI or mobile experiences
• Integrate Power Platform solutions with Microsoft 365 and Azure services
• Ensure best practices for governance and performance optimization
• Collaborate with business stakeholders

Senior Power Platform Developer

• Enable engineering teams
• Partner closely with product engineering teams to understand recurring pain points
• Turn repeated requests into reusable tooling, templates, and documentation
• Contribute to service catalogs, data catalogs, blueprints, and internal documentation
• Coach teams on platform capabilities and engineering best practices
• Build internal platforms and self-service capabilities
• Build and improve internal developer platforms (IDP) that help engineers create, deploy, and operate services with confidence
• Create reusable golden paths for common workflows such as: new service setup and runtime operations, CI/CD onboarding, infrastructure provisioning, and observability setup
• Build internal tools that reduce repetitive manual work and cognitive load
• Improve observability and engineering insight
• Build monitoring, logging, tracing, dashboards, and alerting systems
• Improve visibility into system health, dependencies, and service reliability
• Help define and evolve SLOs, SLIs, and engineering health metrics
• Explore practical AI opportunities
• Apply AI to improve developer productivity, for example: test generation, CI failure analysis, codebase discovery, internal support assistants, and operational troubleshooting

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ML Platform Engineer

Senior Power Platform Developer

Platform Engineer