GenAI Engineer

SATCON Inc; Newark (NJ); On-site; USD 130,000 - 170,000; Full time

Job summary

SATCON Inc is seeking a skilled Cloud Engineer to design, implement, and operate Infrastructure as Code (IaC) using Terraform for Azure-based ML/GenAI deployments, including Azure ML, AKS, and Databricks. You will automate end-to-end pipelines, integrate with CI/CD, and collaborate with data scientists to shape multi-cloud hybrid architectures while ensuring security and compliance.

Ideal candidates have 5+ years in cloud engineering with Terraform for Azure, hands-on ML/GenAI deployment

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent experience).
• 5+ years of experience as a Cloud Engineer, DevOps Engineer, or similar role, with at least 3 years focused on Terraform for IaC in Azure environments.
• Proven expertise in deploying ML/GenAI models using Azure ML services, including model training, registration, endpoints, and inference pipelines.
• Strong hands-on experience with multi-cloud architectures (Azure required; AWS/Google Cloud Platform preferred).
• In-depth knowledge of IaC concepts, including Terraform modules, providers (e.g., AzureRM), variables, outputs, and advanced features like workspaces and backends.
• Solid understanding of Machine Learning lifecycle, including data ingestion, feature engineering, model serving, and scaling in enterprise AI platforms (e.g., Azure ML, SageMaker, Vertex AI).
• Experience with containerization and orchestration tools like Docker, Kubernetes (AKS), and Helm for AI workloads.
• Proficiency in scripting languages such as Python, PowerShell, or Bash for automation.
• Familiarity with security best practices in cloud ML environments, including encryption, access controls, and vulnerability scanning.
• Excellent problem-solving skills and ability to work in agile teams.

Responsibilities

• 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, and related services for ML/GenAI model deployment.
• Deploy and orchestrate ML and GenAI models in enterprise ML platforms, ensuring end-to-end automation from model training to inference, including integration with CI/CD pipelines (e.g., Azure DevOps or GitHub Actions).
• Collaborate with data scientists, ML engineers, and cross-functional teams to architect multi-cloud environments (Azure primary, with AWS/Google Cloud Platform integrations), focusing on hybrid deployments, data sovereignty, and disaster recovery.
• Optimize cloud infrastructure for AI/ML workloads, including compute clusters, storage (e.g., Azure Blob Storage, ADLS), networking (e.g., Virtual Networks, Private Endpoints), and security (e.g., Azure RBAC, Key Vault, Sentinel).
• Implement MLOps practices, such as model versioning, monitoring, logging, and alerting using tools like Azure Monitor, Prometheus, or MLflow to ensure reliable production deployments.
• Develop and enforce IaC best practices, including modular Terraform code, state management (e.g., Azure Storage for remote state), drift detection, and automated testing with tools like Terragrunt or Checkov.
• Troubleshoot and resolve infrastructure issues in production AI environments, ensuring high availability, scalability, and compliance with enterprise standards (e.g., GDPR, SOC 2).
• Conduct code reviews, mentor junior engineers, and contribute to documentation for IaC patterns specific to ML/GenAI use cases.
• Stay updated on emerging Azure ML services (e.g., Azure OpenAI Service, Prompt Flow) and integrate them into multi-cloud IaC frameworks.
• Participate in on-call rotations and incident response for critical AI infrastructure.

Skills

Cloud engineering

DevOps

Terraform IaC

Multi-cloud architecture

Python scripting

Education

Bachelor’s/Master’s in CS or related

Tools

Terraform

Azure ML

Azure DevOps

GitHub Actions

AKS

Azure Databricks

MLflow

Prometheus

Job description

• 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, and related services for ML/GenAI model deployment. [14]
• Deploy and orchestrate ML and GenAI models in enterprise ML platforms, ensuring end-to-end automation from model training to inference, including integration with CI/CD pipelines (e.g., Azure DevOps or GitHub Actions).
• Collaborate with data scientists, ML engineers, and cross-functional teams to architect multi-cloud environments (Azure primary, with AWS/Google Cloud Platform integrations), focusing on hybrid deployments, data sovereignty, and disaster recovery. [12]
• Optimize cloud infrastructure for AI/ML workloads, including compute clusters, storage (e.g., Azure Blob Storage, ADLS), networking (e.g., Virtual Networks, Private Endpoints), and security (e.g., Azure RBAC, Key Vault, Sentinel).
• Implement MLOps practices, such as model versioning, monitoring, logging, and alerting using tools like Azure Monitor, Prometheus, or MLflow to ensure reliable production deployments. [17]
• Develop and enforce IaC best practices, including modular Terraform code, state management (e.g., Azure Storage for remote state), drift detection, and automated testing with tools like Terragrunt or Checkov.
• Troubleshoot and resolve infrastructure issues in production AI environments, ensuring high availability, scalability, and compliance with enterprise standards (e.g., GDPR, SOC 2).
• Conduct code reviews, mentor junior engineers, and contribute to documentation for IaC patterns specific to ML/GenAI use cases.
• Stay updated on emerging Azure ML services (e.g., Azure OpenAI Service, Prompt Flow) and integrate them into multi-cloud IaC frameworks. [11]
• Participate in on-call rotations and incident response for critical AI infrastructure.

Key Responsibilities

• 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, and related services for ML/GenAI model deployment. [14]
• Deploy and orchestrate ML and GenAI models in enterprise ML platforms, ensuring end-to-end automation from model training to inference, including integration with CI/CD pipelines (e.g., Azure DevOps or GitHub Actions).
• Collaborate with data scientists, ML engineers, and cross-functional teams to architect multi-cloud environments (Azure primary, with AWS/Google Cloud Platform integrations), focusing on hybrid deployments, data sovereignty, and disaster recovery. [12]
• Optimize cloud infrastructure for AI/ML workloads, including compute clusters, storage (e.g., Azure Blob Storage, ADLS), networking (e.g., Virtual Networks, Private Endpoints), and security (e.g., Azure RBAC, Key Vault, Sentinel).
• Implement MLOps practices, such as model versioning, monitoring, logging, and alerting using tools like Azure Monitor, Prometheus, or MLflow to ensure reliable production deployments. [17]
• Develop and enforce IaC best practices, including modular Terraform code, state management (e.g., Azure Storage for remote state), drift detection, and automated testing with tools like Terragrunt or Checkov.
• Troubleshoot and resolve infrastructure issues in production AI environments, ensuring high availability, scalability, and compliance with enterprise standards (e.g., GDPR, SOC 2).
• Conduct code reviews, mentor junior engineers, and contribute to documentation for IaC patterns specific to ML/GenAI use cases.
• Stay updated on emerging Azure ML services (e.g., Azure OpenAI Service, Prompt Flow) and integrate them into multi-cloud IaC frameworks. [11]
• 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 experience).
• 5+ years of experience as a Cloud Engineer, DevOps Engineer, or similar role, with at least 3 years focused on Terraform for IaC in Azure environments. [0]
• Proven expertise in deploying ML/GenAI models using Azure ML services, including model training, registration, endpoints, and inference pipelines.
• Strong hands-on experience with multi-cloud architectures (Azure required; AWS/Google Cloud Platform preferred), including cross-cloud networking, identity federation, and resource orchestration.
• In-depth knowledge of IaC concepts, including Terraform modules, providers (e.g., AzureRM), variables, outputs, and advanced features like workspaces and backends.
• Solid understanding of Machine Learning lifecycle, including data ingestion, feature engineering, model serving, and scaling in enterprise AI platforms (e.g., Azure ML, SageMaker, Vertex AI).
• Experience with containerization and orchestration tools like Docker, Kubernetes (AKS), and Helm for AI workloads.
• Proficiency in scripting languages such as Python, PowerShell, or Bash for automation.
• Familiarity with security best practices in cloud ML environments, including encryption, access controls, and vulnerability scanning.
• Excellent problem-solving skills and ability to work in agile teams.

Preferred Qualifications

• Certifications such as Microsoft Certified: Azure DevOps Engineer Expert, Azure AI Engineer Associate, or HashiCorp Certified: Terraform Associate.
• Experience with additional IaC tools like ARM Templates, Bicep, or Pulumi for hybrid Azure setups.
• Background in MLOps tools like Kubeflow, MLflow, or Azure ML Pipelines for enterprise-scale deployments.
• Knowledge of cost optimization in cloud AI environments using tools like Azure Cost Management.
• Prior experience in regulated industries (e.g., finance, healthcare) with compliance-focused IaC.