DevOps Engineer (relocation, Azure)

About us:
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with the organization of the first Data Science UA conference, setting the foundation for our growth. Over the past 9 years, we have diligently fostered the largest Data Science Community in Eastern Europe, boasting a network of over 30,000 AI top engineers.

About the client:
We are working with a new generation of data service provider, specializing in data consulting and data-driven digital marketing, dedicated to transforming data into business impact across the entire value chain of organizations. The company’s data-driven services are built upon the deep AI expertise the company’s acquired with a 1000+ client base around the globe. The company has 1000 employees across 20 offices who are focused on accelerating digital transformation.

About the role:
We are looking for a DevOps Engineer to ensure reliable deployment, automation, and monitoring of AI/ML and cloud applications. The role involves managing CI/CD pipelines, infrastructure automation, and supporting MLOps/AIOps practices.

Required skills:
- 6+ years of experience as a DevOps Engineer or similar role in cloud environments.
- Proficiency in CI/CD tools (Jenkins, GitHub Actions) and infrastructure as code (Terraform).
- Strong hands-on experience with Kubernetes and Azure DevOps ecosystems.
- Experience in scripting with Bash and Python for automation tasks.
- Familiarity with MLOps tools such as MLflow, model monitoring, and drift detection.
- Understanding of AIOps principles for intelligent operations automation.
- Experience ensuring system reliability, observability, and performance optimization.
- Strong collaboration and documentation skills.

Responsibilities:
- Assist in integrating security practices into DevOps processes;
- Support senior engineers in implementing security tools, automating security processes, and ensuring the security of infrastructure and applications.
- Develop and maintain CI/CD pipelines (Jenkins, GitHub Actions).
- Implement infrastructure as code using Terraform and Kubernetes.
- Collaborate with data and AI teams to support MLOps workflows.
- Ensure observability, performance, and security of deployed systems.