Data Engineer - (DevOps and ML) - Ahmedabad, India

We are seeking an experienced DevOps / MLOps Engineer to support a large-scale data and AI platform initiative on Google Cloud Platform (GCP). This role is responsible for building and operating the CI/CD, infrastructure automation, and machine learning lifecycle capabilities that allow data science and engineering teams to move models and data pipelines from experimentation to production reliably, securely, and at scale. The role requires strong expertise in Azure DevOps, cloud infrastructure, containerization, automation, and ML model operations, along with the ability to deliver production-grade platforms aligned with business requirements and project timelines.

Key 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.

Be part of an extraordinary story

Your skills. Your imagination. Your ambition. Here, there are no boundaries to your potential and the impact you can make. You’ll find infinite opportunities to grow and work on the biggest, most rewarding challenges that will build your skills and experience. You have the chance to be a part of our future, and build the life you want while being part of an international community. Our best is here and still to come. To us, impossible is only a challenge. Join us as we dare to achieve what’s never been done before.

Together, everything is possible