Dallas, United States | Posted on 02/02/2026
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
Design, implement, and maintain Infrastructure as Code (IaC) solutions using Terraform to provision and manage Azure resources, including:
- Related services supporting ML and GenAI model deployment
- Develop and enforce IaC best practices , including 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)
- 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
- Optimize cloud infrastructure for AI/ML workloads, including compute clusters
- Ensure infrastructure meets enterprise security, availability, and compliance standards (e.g., GDPR, SOC 2)
MLOps & Observability
- Monitoring
- 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
- 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 managed endpoints and inference pipelines
- Strong hands‑on experience with multi‑cloud architectures (AWS and/or Google Cloud Platform preferred)
- In-depth understanding of Terraform concepts, including modules, variables and outputs, workspaces and backends
- Solid understanding of the machine learning lifecycle
- Experience with containerization and orchestration tools: Docker
- 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
- HashiCorp Certified: Terraform Associate
- Experience with additional IaC tools such as ARM Templates, Bicep, Pulumi (for hybrid Azure setups)
- Background in MLOps tooling , including MLflow
- 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