

DevOps Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a GCP Security Cloud DevOps Engineer focused on Vertex AI, offering a 6–12 month contract-to-hire at a pay rate of "unknown." Key skills include 5+ years in cloud-native ML engineering, GCP expertise, and relevant Google Cloud certifications. Remote work is required.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
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🗓️ - Date discovered
July 9, 2025
🕒 - Project duration
More than 6 months
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🏝️ - Location type
Remote
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📄 - Contract type
1099 Contractor
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🔒 - Security clearance
Unknown
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📍 - Location detailed
United States
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🧠 - Skills detailed
#Data Management #TensorFlow #Deployment #Storage #GitHub #AWS (Amazon Web Services) #DevOps #Kubernetes #Scala #AI (Artificial Intelligence) #Containers #Dataflow #Terraform #ML (Machine Learning) #GCP (Google Cloud Platform) #Security #Automation #BigQuery #Azure #PyTorch #Python #Cloud
Role description
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Job Title: GCP Security Cloud DevOps Engineer / Vertex AI Engineer
Remote: U.S.-based required
Duration: 6–12 months contract-to-hire
Interviews: 3–4 rounds
Team: Enterprise Cloud Security – AI Cloud Security Team
Summary:
We are standing up a new AI-focused team within the Cloud Security group at a nationally recognized enterprise-scale organization. This role is for a hands-on Vertex AI Engineer focused on building scalable ML frameworks in Google Cloud with a direct application in cloud security. You'll be part of a high-performance group working across Azure, GCP, AWS, containers, and applied AI. This is a net-new position with significant influence and opportunity.
Scope of Work:
• Build, deploy, and manage ML models in Vertex AI
• Architect and implement AI-driven security automation across GCP
• Create enforcement logic and automated remediation using Python, Terraform, and GitHub Actions
• Stand up ML pipelines with Dataflow, Dataproc, BigQuery, and GCP-native tools
• Collaborate with cloud security engineers to establish governance rules and codify them in infrastructure
• Research and prototype agentic and cognitive AI approaches relevant to threat detection and response
• Support deployment in GKE environments with a focus on scalable, secure orchestration
Key Technologies:
• Vertex AI (required): End-to-end model lifecycle management including development, training, and deployment
• GKE: For containerized inference and scalable deployment
• BigQuery, Dataflow, Dataproc, Cloud Storage: For training data management and pipeline operations
• Python, Terraform, GitHub Actions: For infrastructure and orchestration
• ML Frameworks: TensorFlow, PyTorch, scikit-learn, XGBoost
Qualifications:
• 5+ years of hands-on experience in cloud-native ML engineering
• 5+ years working in GCP with deep expertise in Vertex AI
• Strong grounding in machine learning model design, training, and operationalization
• Proven experience in applying AI/ML within a cloud security context
• Expert-level knowledge of Kubernetes / GKE in production environments.
• Experience deploying secure AI solutions in enterprise GCP environments
• Familiarity with CSPM tools such as Prisma or Wiz is a plus
Google Cloud Certifications:
• Cloud Digital Leader or above – Required
• Associate Cloud Engineer or above – Required
• Professional Cloud Architect – Highly Preferred
• Professional DevOps Engineer – Highly Preferred
Preferred Traits:
• Operates like an internal Special-Ops team member—fast, precise, and collaborative
• Comfortable in fast-forming teams solving unsolved problems
• Capable of applying passion for researching emerging AI security trends into translating them into engineering deliverables
Note:
Candidates should be comfortable operating under a 1099 model. HIPAA and Security Awareness Training (provided, ~45 mins total) will be required prior to client access.