MLOps Engineer

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.

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

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