MLOps Engineer

XM is a leading international FinTech company, committed to shaping the future of online trading through innovative solutions. Established in 2009, XM has grown exponentially with a global team of over 1400 employees around the world. Headquartered in Cyprus, XM also operates from offices in Greece, UK, UAE, USA, South Africa, and Uruguay. Guided by its core values, Big. Fair. Human., XM consistently ranks as a top-rated workplace, having received Platinum accreditation from Investors In People, alongside consistent recognition as one of the top Best Workplaces™.

• Bachelor's degree in Computer Science, Engineering, or related field
• 2+ years of hands-on experience in MLOps, DevOps, or related fields
• Knowledge and preferable working experience in 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 working 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 relevant 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 closely 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 the 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 to ensure continuous model improvement