MLOps Engineer

XM is a leading international FinTech company, established in 2009, with a global team of over 1400 employees. Headquartered in Cyprus, XM operates offices in Greece, UK, UAE, USA, South Africa, and Uruguay. The company is recognized as a top-rated workplace with Platinum accreditation from Investors In People.

• Bachelor’s degree in Computer Science, Engineering, or related field
• 2+ years of hands-on experience in MLOps, DevOps, or related fields
• Knowledge and experience with AWS services for machine learning such as SageMaker, EKS, S3, EC2, Lambda
• Exposure to Kubernetes for container orchestration
• Experience with Docker
• Exposure to infrastructure-as-code tools such as Terraform or CloudFormation
• Familiarity with CI/CD tools such as GitLab CI
• Understanding of machine learning model lifecycle
• Familiarity with monitoring and logging solutions like Prometheus, Grafana, CloudWatch, and ELK Stack
• Understanding of networking concepts and cloud security best practices
• Proficiency in Python and Bash, comfortable working in Linux environments
• Strong problem-solving and communication skills

Nice to have:

• Experience with serverless architectures and event-driven processing on AWS
• Familiarity with advanced Kubernetes concepts such as Helm
• Experience with Data Engineering pipelines, ETL processes, or big data platforms
• Experience with ML frameworks like TensorFlow, PyTorch, and Keras
• Experience with ML platforms like Kubeflow and/or SageMaker
• Experience with workflow engines like Argo Workflows and/or Airflow

• Assist in designing, implementing, and maintaining scalable MLOps pipelines on AWS using services such as SageMaker, EC2, EKS, S3, Lambda, and other AWS tools
• Coordinate with platform team to troubleshoot Kubernetes clusters (EKS) for deployment of machine learning models and microservices
• Develop and maintain CI/CD pipelines for model and application deployment, testing, and monitoring
• Collaborate with Data Science and DevOps teams to streamline the model development lifecycle from experimentation to production
• Implement security best practices including network security, data encryption, and role-based access controls within AWS infrastructure
• Monitor, troubleshoot, and optimize data and ML pipelines to ensure high availability and performance
• Set up and manage model monitoring systems for performance drift and continuous model improvement