AI Infrastructure Engineer
4 weeks ago
Are you passionate about designing and building the robust infrastructure that powers cutting-edge AI solutions? Do you thrive on creating scalable, high-performance systems that support AI workloads, from training machine learning models to deploying real-time inference? If you're excited about building the backbone for the future of AI, then our client has the perfect opportunity for you. We’re looking for an AI Infrastructure Engineer (aka The AI Backbone Builder) to design, deploy, and maintain the infrastructure that powers AI innovation.
As an AI Infrastructure Engineer at our client, you’ll play a critical role in building the platforms that support machine learning and AI development across the organization. You’ll work closely with data scientists, software engineers, and DevOps teams to ensure that AI systems run efficiently, securely, and at scale. Your work will enable fast experimentation, seamless deployments, and the continuous delivery of AI models into production.
Key Responsibilities:
- Design and Build AI Infrastructure: Architect and implement scalable infrastructure that supports AI workloads, including machine learning model training, large-scale data processing, and real-time inference. You’ll design solutions that ensure high availability, fault tolerance, and performance optimization.
- Support AI Model Development and Deployment: Collaborate with data scientists and engineers to build pipelines that automate the end-to-end machine learning lifecycle, from data ingestion to model training, deployment, and monitoring. You’ll ensure smooth integration of AI models into production environments.
- Optimize AI Workloads for Performance: Implement strategies to optimize compute resources for AI workloads, including GPU/TPU provisioning, memory management, and parallel processing. You’ll ensure that infrastructure is optimized for the unique demands of AI and machine learning tasks.
- Cloud and On-Premise Infrastructure Management: Manage cloud-based AI platforms (AWS, GCP, Azure) as well as on-premise infrastructure for AI development. You’ll handle everything from infrastructure as code (IaC) to container orchestration (Docker, Kubernetes), ensuring seamless scalability and automation.
- Automation and Continuous Integration/Deployment (CI/CD): Implement and maintain CI/CD pipelines for machine learning models to enable rapid experimentation, testing, and deployment. You’ll automate workflows, model updates, and monitor the performance of AI systems in production.
- Security and Compliance: Ensure that the AI infrastructure complies with security best practices and regulatory requirements. You’ll implement robust access controls, encryption, and other security measures to protect sensitive data and AI models.
- Monitor and Troubleshoot AI Infrastructure: Continuously monitor the health and performance of AI infrastructure, identifying bottlenecks, reducing latency, and troubleshooting issues. You’ll ensure the reliability of systems, optimizing them as AI demands grow.
Required Skills:
- AI Infrastructure Expertise: Deep experience in designing and building infrastructure that supports AI and machine learning workloads. You’re familiar with both cloud and on-premise infrastructure solutions and know how to optimize them for AI.
- Cloud Platforms and Tools: Strong experience with cloud platforms like AWS, GCP, or Azure, particularly with AI services and infrastructure management. You’re comfortable with tools like SageMaker, AI Platform, or Azure ML, as well as container orchestration with Kubernetes.
- Automation and DevOps: Expertise in automating infrastructure provisioning and model deployment using tools such as Terraform, Ansible, Jenkins, or GitLab CI. You’re skilled at managing CI/CD pipelines for AI model deployment.
- GPU/TPU Optimization: Hands-on experience with GPU/TPU optimization for machine learning and deep learning tasks. You understand how to manage compute resources to maximize efficiency for AI workloads.
- Security and Compliance: Strong understanding of security best practices, including data encryption, access management, and compliance with regulations like GDPR and HIPAA.
Educational Requirements:
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field. Equivalent experience in AI infrastructure or DevOps is highly valued.
- Certifications in cloud platforms (AWS, GCP, Azure) or DevOps tools are a plus.
Experience Requirements:
- 3+ years of experience in infrastructure engineering, with a focus on building and maintaining AI or machine learning infrastructure in production environments.
- Proven experience with cloud services, containerization, orchestration tools, and optimizing infrastructure for AI workloads.
- Experience working with data scientists and machine learning engineers to support model development, testing, and deployment.
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