Senior Terraform Engineer - Azure-Centric (ML & GenAI Focus)

Location: Charlotte, NC
Employment Type: Contract 
Permanent Residents and

We are seeking a highly skilled Senior Terraform Engineer with deep expertise in Azure services to join our Enterprise AI Platform team. This role is Azure-centric, with a strong emphasis on deploying Machine Learning (ML) and Generative AI (GenAI) models in scalable, secure, enterprise environments.

The ideal candidate will have hands-on experience with multi-cloud architectures, Infrastructure as Code (IaC) best practices, and a strong foundation in ML workflows, enterprise AI platforms, and cloud-based ML services. You will play a key role in automating infrastructure provisioning, integrating AI/ML pipelines, and optimizing deployments for performance, cost, security, and compliance across a multi-cloud landscape.

This position requires a proactive engineer who can bridge DevOps and MLOps, leveraging Terraform to support high-impact AI initiatives. If you thrive in fast-paced environments and are passionate about building robust, automated cloud infrastructures for AI at scale, this role offers a unique opportunity to drive innovation.

Key Responsibilities

Infrastructure as Code & Azure Platform Engineering

• Design, implement, and maintain Infrastructure as Code (IaC) solutions using Terraform to provision and manage Azure resources, including:

  • Azure Machine Learning (Azure ML)

  • Azure AI Studio

  • Azure Kubernetes Service (AKS)

  • Azure Databricks

  • Related services supporting ML and GenAI model deployment

• Develop and enforce IaC best practices, including:

  • Modular Terraform design

  • Remote state management (Azure Storage backends)

  • Drift detection

  • Automated policy and security testing using tools such as Terragrunt and Checkov

ML & GenAI Platform Enablement

• Deploy and orchestrate ML and GenAI models on enterprise ML platforms

• Enable end-to-end automation across the ML lifecycle, from model training through inference

• Integrate AI/ML workflows with CI/CD pipelines (Azure DevOps, GitHub Actions)

Multi-Cloud Architecture & Integration

• Collaborate with data scientists, ML engineers, and cross-functional teams to design multi-cloud architectures, with Azure as the primary platform and AWS/Google Cloud Platform integrations

• Support hybrid deployments, data sovereignty requirements, and disaster recovery strategies

• Implement cross-cloud networking, identity federation, and resource orchestration

Cloud Optimization & Security

• Optimize cloud infrastructure for AI/ML workloads, including:

  • Compute clusters

  • Storage (Azure Blob Storage, Azure Data Lake Storage – ADLS)

  • Networking (Virtual Networks, Private Endpoints)

  • Security controls (Azure RBAC, Azure Key Vault, Azure Sentinel)

• Ensure infrastructure meets enterprise security, availability, and compliance standards (e.g., GDPR, SOC 2)

MLOps & Observability

• Implement MLOps best practices, including:

  • Model versioning

  • Monitoring

  • Logging

  • Alerting

• Leverage observability tools such as Azure Monitor, Prometheus, and MLflow to ensure reliable, production-grade deployments

Operations & Collaboration

• Troubleshoot and resolve infrastructure issues in production AI environments

• Ensure high availability, scalability, and reliability of AI platforms

• Conduct code reviews, mentor junior engineers, and contribute to documentation for ML/GenAI-specific IaC patterns

• Stay current with emerging Azure ML services, including:

  • Azure OpenAI Service

  • Prompt Flow

• Participate in on-call rotations and incident response for critical AI infrastructure

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent professional experience)

• 5 years of experience as a Cloud Engineer, DevOps Engineer, or similar role

• At least 3 years of hands-on experience with Terraform for IaC in Azure environments

• Proven experience deploying ML and GenAI models using Azure ML, including:

  • Model training

  • Model registration

  • Managed endpoints

  • Inference pipelines

• Strong hands-on experience with multi-cloud architectures

  • Azure required

  • AWS and/or Google Cloud Platform preferred

• In-depth understanding of Terraform concepts, including:

  • Modules

  • Providers (AzureRM)

  • Variables and outputs

  • Workspaces and backends

• Solid understanding of the machine learning lifecycle, including:

  • Data ingestion

  • Feature engineering

  • Model serving

  • Scaling in enterprise AI platforms (Azure ML, SageMaker, Vertex AI)

• Experience with containerization and orchestration tools:

  • Docker

  • Kubernetes (AKS)

  • Helm

• Proficiency in scripting languages such as Python, PowerShell, or Bash

• Familiarity with cloud security best practices for ML environments, including:

  • Encryption

  • Access controls

  • Vulnerability scanning

• Strong problem-solving skills and experience working in Agile teams

Preferred Qualifications

• Relevant certifications, including:

  • Microsoft Certified: Azure DevOps Engineer Expert

  • Azure AI Engineer Associate

  • HashiCorp Certified: Terraform Associate

• Experience with additional IaC tools such as:

  • ARM Templates

  • Bicep

  • Pulumi (for hybrid Azure setups)

• Background in MLOps tooling, including:

  • Kubeflow

  • MLflow

  • Azure ML Pipelines

• Experience with cloud cost optimization for AI workloads using tools like Azure Cost Management

• Prior experience working in regulated industries (finance, healthcare, etc.) with compliance-driven infrastructure requirements