What are the responsibilities and job description for the Azure Platform Architect with AI/ML & Gen AI position at AceStack?
Job Title: Azure Platform Architect with AI/ML & Gen AI
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.
