Job Description

The main responsibilities of the position include:

• 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 our platform team to troubleshoot Kubernetes clusters (EKS) to orchestrate the deployment of machine learning models and other microservices
• Develop and maintain CI/CD pipelines for model and application deployment, testing, and monitoring
• Collaborate closely with Data Science, and DevOps team to streamline the model development lifecycle, from experimentation to production deployment
• 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, ensuring continuous model improvement

Main requirements:

• Bachelor’s degree in Computer Science, Engineering, or related field
• 1+ 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, and others
• 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 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, and comfortable working in Linux environments
• Strong problem-solving and communication skills

The following will be considered an advantage:

• 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

Benefit from:

• Attractive remuneration package plus performance related reward
• Private health insurance
• Corporate pension fund
• Intellectually stimulating work environment
• Continuous personal development and international training opportunities

The Hiring Experience: What Awaits You

• Let’s Connect – Intro Chat with Talent Acquisition
• Deep Dive – First Interview with Your Future Team
• Final Connection – Final Interview