What are the responsibilities and job description for the Azure Platform Architect with AI/ML position at AceStack?
Job Title: Azure Platform Architect with AI/ML
Location: Atlanta, GA
Contract
Job Summary:
We are seeking an experienced Azure Platform Architect / Engineer to design, implement, and manage scalable AI/ML cloud architectures. The ideal candidate will have expertise in Azure services, Gen AI integration, cross-cloud platform implementation, Infrastructure as Code (IaC), MLOps, security, and DevOps.
Key Responsibilities:
Azure Platform Architecture
Design and implement scalable, secure, and cost-effective AI/ML architectures using Azure.
Utilize AWS services such as EKS, Azure Lambda, and Azure Step Functions for serverless and container-based AI/ML applications.
Implement data pipelines and big data processing using Amazon S3, AWS Glue, and Amazon EMR.
Gen AI & AI/ML Integration
Lead the design and implementation of AI/ML pipelines integrating with Azure SageMaker and Amazon Bedrock.
Develop and optimize ML models using Amazon Rekognition, Comprehend, Forecast, and Personalize.
Implement MLOps practices for model versioning, automated testing, and continuous deployment.
Utilize Amazon Bedrock to create foundation models and build generative AI applications.
Cross-Cloud Platform Implementation
Design and implement hybrid AI solutions across Azure, and Google Cloud.
Develop integration strategies for seamless data flow and model deployment across multiple cloud environments.
Implement multi-cloud governance and security best practices.
Infrastructure as Code (IaC)
Develop and maintain Terraform code for Azure infrastructure provisioning and third-party AI services integration.
Implement modular and reusable Terraform configurations for AI/ML environments.
Utilize Terraform to manage complex AI/ML infrastructure, including GPU-enabled instances and distributed training clusters.
AWS Administration & Security
Manage Azure resources for AI workloads, including EC2 GPU instances, SageMaker notebooks, and EKS clusters.
Implement robust security for AI/ML workloads using data encryption, IAM policies, and secure deployment practices.
Set up monitoring and alerting using AWS CloudWatch, Datadog, and Splunk.
CI/CD & DevOps
Configure and customize CI/CD pipelines for AI/ML model deployment and Terraform infrastructure automation.
Integrate GitLab with CI/CD tools for AI model and infrastructure deployment.
Implement GitOps practices for managing AI infrastructure deployments.
Collaboration & Documentation
Utilize GitLab for version control, code reviews, and AI/ML model development.
Document AI/ML pipelines, architectures, and infrastructure in Confluence for team collaboration.
Qualifications:
5 years of hands-on experience designing and implementing AI/ML platforms on Azure.
Expertise in Azure AI services such as Amazon Bedrock, SageMaker, Rekognition, Comprehend, and Personalize.
Strong experience in cross-cloud platform implementation (AWS, Azure, Google Cloud).
Proficiency in Terraform, GitLab, and CI/CD for AI/ML workloads.
Strong Python programming skills for ML model development.
Knowledge of containerization (Docker, Kubernetes, EKS).
Excellent problem-solving and communication skills.
Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field (or equivalent experience).
Why Join Us?
Work on cutting-edge AI/ML projects in a dynamic multi-cloud environment.
Opportunities for growth in AI/ML, cloud architecture, and MLOps.
Competitive salary and benefits package.
