Data Engineer - (DevOps and ML) - Ahmedabad, India
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Protocol Intelligence
Data-driven signals on your job's competitivenessSpecialized MLOps/devops skills reduce applicants, but a popular 'Data Engineer' title and mid-level role raise competition.
Platform DevOps and MLOps skills transfer across industries, though aviation/regulatory familiarity is beneficial.
Many mandatory technologies, cloud certifications, and platform experience make screening rigorous and selective.
Job Description
Structured overview of role & requirementsAbout This Role
Build and maintain Azure DevOps CI/CD pipelines for data pipelines, ML models, and apps deployed on Google Cloud Platform (GCP).
Manage GCP infrastructure via Terraform and automate ML lifecycle operations using Vertex AI and related services.
Implement monitoring, security, governance, and cost optimization practices across cloud and ML platforms ensuring reliable production deployments.
Minimum Requirements
Strong hands-on expertise with Azure DevOps (YAML pipelines, Repos, Artifacts, environments).
Proven experience with GCP services including GKE, Cloud Run, Vertex AI, BigQuery, Cloud Storage, Composer, and IAM.
Experience in cloud infrastructure as code using Terraform and container orchestration with Docker and Kubernetes (GKE).
Work Experience Required: Not explicitly mentioned in the JD.
Ideal Candidate Profile
Experienced in operationalizing ML models and managing end-to-end MLOps workflows on GCP with Azure DevOps integration.
Background in large-scale cloud migration projects moving on-premise data and ML workloads to cloud.
Proficient in DevSecOps practices, cross-platform automation, and cloud cost-performance optimization aligned with regulated enterprise environments.