The Role:
We are looking for a skilled MLOps Engineer to join our team and play a key role in bridging the gap between our Data Science and Infrastructure teams. You will be responsible for supporting the Data Science team in MLOps-related tasks while also helping in DevOps initiatives, including CI/CD pipeline creation, provisioning of cloud resources using tools like Terraform, and Kubernetes orchestration. You will collaborate closely with data scientists and engineers to deploy data pipelines, train machine learning models, and manage their deployment within scalable cloud environments while ensuring high performance, security, and reliability throughout the ML lifecycle.
All applications will be treated with strict confidentiality!
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
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, 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
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
