As an AI DevOps/SRE Engineer, you will be pivotal in deploying, maintaining, and scaling our AI solutions, including LLMs and RAG systems. You will work closely with data scientists and software developers to ensure seamless integration and operational efficiency of our AI deployments. Your role will involve both classic DevOps tasks and innovative approaches to MLOps, ensuring high availability and optimal performance of our systems.

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

We Offer

Build a global career with international projects and clients
Stay ahead in your career by working with diverse and cutting-edge technologies
Competitive compensation in USD, regular assessments, and salary reviews
Private Health Insurance: Unlimited usage with 80% coverage
Meal Allowance
Extensive Annual Leave Policy including extra workday-annual leaves granted for the first year
Referral program: cash bonus for each successful recommendation
Relocation opportunities within our offices in 50+ countries
Amazing learning and development opportunities: Hard & soft skills internal training courses, mentoring programs, and free access to over 2,500 in-house courses and over 18,000 on LinkedIn learning