Senior/Lead AI DevOps/SRE
We are currently seeking an experienced Lead AI DevOps/SRE to join our team.
In this pivotal role, you will collaborate closely with data scientists and software developers to ensure seamless integration and optimize the operational efficiency of our AI deployments. Your expertise will be pivotal in deploying, maintaining, and scaling our cutting-edge AI solutions, encompassing LLMs and RAG systems.
As a key team member, you will spearhead both traditional DevOps responsibilities and innovative approaches to MLOps. Your proactive involvement will be essential in driving the success of our AI initiatives and maximizing their impact across the organization.
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
- Knowledge of Generative AI Operations
- Expertise in deployment and management of LLMs, including technologies like RAG
- Proficient in CI/CD tools (Jenkins, GitLab CI, CircleCI) and infrastructure as code (Terraform, Ansible)
- Solid knowledge of container orchestration technologies (Kubernetes, Docker)
- Familiarity with MLOps tools and practices to support machine learning lifecycle management
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
- Understanding of Python, particularly in data science and machine learning contexts
- Certification in Kubernetes, AWS/GCP/Azure, or similar technologies
