AI DevOps / SRE Engineer

Responsibilities

• Implement and maintain CI/CD pipelines for AI and machine learning projects, ensuring robust deployment strategies and continuous integration
• Monitor and ensure the reliability, availability, and performance of AI applications, particularly those involving LLMs and RAG
• Collaborate with AI research teams to operationalize machine learning models and systems efficiently
• Develop and enforce best practices for version control, configuration management, and testing of AI-driven software solutions
• Utilize MLOps tools such as Kubeflow, MLflow, or TensorFlow Extended (TFX) to streamline the machine learning lifecycle from experimentation to production
• Implement monitoring solutions that track both system metrics and model performance to facilitate proactive issue resolution
• Participate in on-call rotations to support the operational health of critical systems, employing SRE principles to meet service-level objectives (SLOs) and reduce downtime

Requirements

• Bachelor’s degree in Computer Science, Engineering, or a related field
• Proven experience as a DevOps Engineer or SRE, with a strong background in software development and automation
• Experience with deployment and management of LLMs, including technologies like RAG
• Proficient in CI/CD tools (e.g., Jenkins, GitLab CI, CircleCI) and infrastructure as code (e.g., Terraform, Ansible)
• Knowledge of container orchestration technologies (e.g., Kubernetes, Docker)
• Familiarity with MLOps tools and practices to support machine learning lifecycle management
• Strong problem-solving skills and ability to work in a dynamic, fast-paced environment

Nice to have

• Experience with cloud services (AWS, GCP, Azure) particularly in AI/ML deployments
• Background in monitoring tools like Prometheus, Grafana, and ELK stack
• Knowledge of Python, particularly in data science and machine learning contexts
• Certification in Kubernetes, AWS/GCP/Azure, or similar technologies