Overview

Corporate & Commercial

Responsibilities

• Design, build, and maintain CI/CD pipelines in Azure DevOps (YAML pipelines, Azure Repos, Azure Artifacts, service connections, environments, and approval gates) for data pipelines, ML models, and application services deployed to GCP.
• Provision and manage GCP infrastructure using Infrastructure as Code (Terraform) executed through Azure Pipelines, covering GKE, Cloud Run, BigQuery, Cloud Storage, Vertex AI, Composer, and networking/IAM.
• Build and operate end-to-end MLOps workflows on Vertex AI (or equivalent), including feature stores, training pipelines, model registry, automated evaluation, and deployment to batch and online endpoints, triggered and governed through Azure DevOps.
• Containerize and orchestrate workloads with Docker and Kubernetes (GKE), including Helm charts, autoscaling, and resource optimization for training and inference jobs.
• Implement model monitoring for drift, data quality, performance degradation, and cost, with automated alerting and retraining triggers.
• Establish reproducibility and governance practices: experiment tracking (MLflow / Vertex Experiments), data and model versioning, lineage, branching strategies, and promotion gates across dev, UAT, and production environments.
• Implement observability across platforms and services using Cloud Monitoring, Cloud Logging, Prometheus, and Grafana, and define SLOs and incident response processes.
• Embed security and compliance into the delivery lifecycle: secrets management (Azure Key Vault / GCP Secret Manager), IAM least privilege, vulnerability scanning, image signing, and policy-as-code within pipelines.
• Support migration of on-premises data and ML workloads to GCP, including redesign of build, deployment, and orchestration patterns where required.
• Optimize cloud cost and performance across compute, storage, and ML serving resources.
• Collaborate with data scientists, data engineers, and application teams to standardize pipeline templates, development environments, and deployment patterns; manage Azure Boards work items and release planning where applicable.
• Participate in UAT, release management, production support, and documentation of platform standards and runbooks.
• Strong hands-on expertise in:
• Azure DevOps (YAML pipelines, Azure Repos, Azure Artifacts, environments, approvals, service connections, pipeline templates)
• GCP services (GKE, Cloud Run, Vertex AI, BigQuery, Cloud Storage, Composer, IAM)
• Terraform and Infrastructure as Code practices
• Docker, Kubernetes, and Helm
• Proven experience deploying to GCP from Azure DevOps, including Workload Identity Federation or service account integration and multi-environment release strategies.
• Strong proficiency in Python and shell scripting; working knowledge of SQL.
• Proven experience operationalizing ML models, including pipeline orchestration (Vertex AI Pipelines, Kubeflow, or Airflow), model registry, and serving (batch and real-time).
• Solid understanding of ML lifecycle concepts: experiment tracking, feature engineering, model validation, drift monitoring, and retraining strategies.

Qualifications

• Migration of ML or data workloads to managed cloud services
• Familiarity with data engineering tools in the GCP ecosystem (Dataflow, Dataproc, Pub/Sub) is preferred.
• Google Cloud Professional certifications
• Exposure to Azure cloud services or multi-cloud environments
• Proven experience in cloud migration projects, including:
• Knowledge of aviation, travel, or large-enterprise regulated environments
• Ability to work closely with data science and business stakeholders to translate requirements into platform capabilities.
• Excellent communication and collaboration skills.
• Ability to manage multiple platforms, environments, and release scenarios effectively.