DevOps Engineer (AI/ML) - Onsite in Palo Alto, CA

Title : DevOps Engineer

Duration : Full-Time/Direct Hire

Location : Palo Alto, CA (On-site)

Salary : $80K - $175K DOE

You must be able to work on-site in Palo Alto, CA to be considered for this position.

Job Overview:

Our client that develops cutting-edge Artificial General Intelligence (AGI) solutions is seeking a DevOps Engineer with a passion for AI/ML technologies to join their dynamic team. You will play a critical role in managing infrastructure both in the cloud and on-premise, ensuring seamless operations for our internal teams and external customers. If you are curious about AI and excited to work in an environment that bridges DevOps and AI development, this role is perfect for you.

Key Responsibilities:

• Infrastructure Management: Set up, maintain, and optimize cloud and on-premise environments for AI/ML workloads, ensuring scalability, security, and reliability.
• Automation: Develop and maintain CI/CD pipelines for AI/ML model training, deployment, and testing across multiple environments.
• Collaboration: Work closely with data scientists, ML engineers, and software developers to streamline the development-to-production process.
• Machine Learning Operations (MLOps): Implement MLOps best practices to support the AI/ML team in their model lifecycle, from training to deployment and monitoring.
• Cloud Services: Manage cloud infrastructure on platforms such as AWS, Google Cloud, or Azure, ensuring cost-efficient and high-performance resource allocation for model training and deployment.
• On-Premise Solutions: Configure and manage on-premise hardware for training models, ensuring hardware is optimized for AI tasks (e.g., GPU/TPU configurations).
• Monitoring & Troubleshooting: Build robust monitoring and alerting systems to proactively identify and solve issues related to infrastructure and application performance.
• Security & Compliance: Implement and enforce security best practices across all platforms, both cloud and on-prem, including role-based access control and data encryption.
• Customer Support: Provide technical support to customers in deploying and managing their AI workloads, assisting with integration and troubleshooting.

Qualifications:

• 3+ years in DevOps, with a focus on managing infrastructure for data or AI-driven environments.
• Cloud Expertise: Hands-on experience with AWS, Azure, Google Cloud, or similar platforms.
• On-Prem Experience: Knowledge of managing and scaling on-prem hardware for AI tasks, including GPU/TPU resources.
• Automation Tools: Experience with CI/CD pipelines, Docker, Kubernetes, and configuration management tools like Ansible, Terraform, or Puppet.
• MLOps: Exposure to AI/ML frameworks (e.g., TensorFlow, PyTorch) and familiarity with MLOps pipelines is a plus.
• Scripting & Programming: Proficiency in scripting languages such as Python, Bash, or Go for automating workflows.
• Version Control: Expertise with Git and related source control tools.
• Problem-Solving: Strong troubleshooting skills, especially in high-performance, data-heavy environments.
• AI Enthusiast: Curiosity about AI/ML technologies, with a desire to learn and grow in the space.

Nice-to-Have Skills:

• Experience with AI model serving frameworks like TensorFlow Serving, TorchServe, or KubeFlow.
• Familiarity with monitoring tools such as Prometheus, Grafana, or Elastic Stack.
• Knowledge of networking and security best practices for hybrid environments.