CloudOps - MLOps

Position Description:

The SRE team at Aurigo provides hosting, infrastructure lifecycle management, security, and scaling support for Aurigos flagship AI/ML-powered products on AWS Cloud Infrastructure. Within SRE, the CloudOps function focuses on ensuring the reliability, scalability, and performance of applications, Infrastructure and data pipelines in production.

To deliver cutting-edge AI-driven capabilities to our products, we need to design, implement, and manage highly available, resilient, and scalable cloud infrastructures tailored for ML workloads. Aurigo is looking for a dynamic CloudOps Engineer with expertise in both ML model lifecycle management and cloud infrastructure administration. The engineer will be responsible for ensuring a highly available and reliable ML environment, handling tasks such as model deployment, pipeline automation, system administration, and release engineering. Given the computational and data-intensive nature of ML workloads, maintaining optimal performance and reliability is critical. We are committed to achieving 99.99% uptime for our platforms and services.

Responsibilities:

• Utilize AWS AI services such as SageMaker, Bedrock, and other AWS ML tools for model training and deployment.
• Develop and manage data and model workflows using Kubeflow, Apache Airflow, or other workflow orchestration tools.
• Ensure model governance, versioning, lineage tracking, and monitoring to maintain compliance and auditability.
• Optimize and scale ML workloads using containerized solutions such as Docker and orchestration platforms like Kubernetes (EKS).

• Design, build, and manage CI/CD pipelines for ML models, including training, testing, and deployment.
• Implement MLOps best practices for automating and monitoring ML workflows.
• Maintain and enhance AI/ML infrastructure deployment processes, focusing on model performance optimization and cost efficiency.
• Work closely with Data Scientists, ML Engineers, and CloudOps teams to ensure seamless ML model deployment and lifecycle management.

Required Skills & Qualifications

• 3-6 years of experience in CloudOps, or DevOps teams focusing on MLOps.
• Hands-on experience with AWS AI/ML services such as SageMaker, Bedrock, Lambda, and S3 etc.
• Strong expertise in CI/CD automation for ML using tools like GitLab CI/CD, Jenkins, Azure DevOps or AWS CodePipeline.
• Experience with workflow orchestration using Kubeflow, Apache Airflow, or Step Functions.
• Proficiency in containerization technologies (Docker, Kubernetes, AWS EKS).
• Strong scripting and automation skills using Python, Bash
• Knowledge of model monitoring, logging, and lineage tracking for governance and compliance.
• Understanding of ML model lifecycle, feature stores, and data engineering workflows.