Senior Systems Engineer - Data DevOps/MLOps

We're seeking a motivated, detail-oriented Senior Systems Engineer specializing in Data DevOps/MLOps to join our team.
The right candidate will have in-depth knowledge of data engineering, data pipeline automation, and the integration of machine learning models into live environments. This position suits a collaborative individual skilled at creating, launching, and overseeing scalable data and ML pipelines that support strategic goals.
Responsibilities
Design CI/CD pipelines for data integration and machine learning model deployment
Deploy and maintain cloud-based infrastructure for data processing and model training
Automate workflows such as data validation, transformation, and orchestration
Collaborate with data scientists, software engineers, and product teams to bring ML models into production
Improve performance and reliability through optimized model serving and monitoring
Maintain data versioning, lineage tracking, and reproducibility throughout ML experiments
Pinpoint opportunities to strengthen deployment processes, scalability, and infrastructure resilience
Apply security protocols to protect data integrity and ensure compliance
Troubleshoot and resolve issues across the data and ML pipeline lifecycle
Requirements
Bachelor's or Master's degree in Computer Science, Data Engineering, or a related discipline
At least 5 years of experience in Data DevOps, MLOps, or similar roles
Hands-on experience with cloud platforms such as Azure, AWS, or GCP
Working knowledge of Infrastructure as Code (IaC) tools like Terraform, CloudFormation, or Ansible
Strong command of containerization and orchestration tools such as Docker and Kubernetes
Experience with data processing frameworks like Apache Spark or Databricks
Solid Python skills, including familiarity with data and ML libraries such as Pandas, TensorFlow, or PyTorch
Exposure to CI/CD tools like Jenkins, GitLab CI/CD, or GitHub Actions
Understanding of version control systems like Git and MLOps platforms such as MLflow or Kubeflow
Familiarity with monitoring, logging, and alerting tools like Prometheus or Grafana
Sharp problem-solving skills with the ability to thrive independently or as part of a team
Strong communication and documentation abilities
Nice to have
Exposure to DataOps practices and tools such as Airflow or dbt
Awareness of data governance frameworks and tools like Collibra
Familiarity with Big Data technologies such as Hadoop or Hive
Certifications in cloud platforms or data engineering disciplines

Work arrangement
Hybrid