Senior Systems Engineer - Data DevOps/MLOps

We're seeking a motivated, detail-orientedSenior 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