Senior Systems Engineer - Data DevOps/MLOps

Our team is seeking a skilled and committed Senior Systems Engineer who brings deep expertise in Data DevOps/MLOps to strengthen our organization.
The successful applicant will demonstrate a thorough understanding of data engineering practices, automated data pipelines, and the operational deployment of machine learning models. This position requires a collaborative individual capable of architecting, implementing, and overseeing large-scale data and ML pipelines that support our company's goals.
Responsibilities
Build, launch, and oversee CI/CD pipelines supporting data integration and ML model rollout
Establish and maintain cloud-based infrastructure for data processing and model training operations
Streamline data validation, transformation, and workflow orchestration through automation
Partner with data scientists, software engineers, and product teams to ensure seamless ML model integration into production environments
Improve model serving and monitoring capabilities to increase performance and reliability
Oversee data versioning, lineage tracking, and ensure ML experiments remain reproducible
Continuously identify opportunities to improve deployment workflows, scalability, and infrastructure durability
Enforce robust security measures that protect data integrity and meet regulatory standards
Diagnose and resolve problems across the entire data and ML pipeline lifecycle
Requirements
Bachelor's or Master's degree in Computer Science, Data Engineering, or related discipline
Minimum 5 years of relevant experience in Data DevOps, MLOps, or comparable positions
Skilled in cloud platforms such as Azure, AWS, or GCP
Experience working with Infrastructure as Code tools like Terraform, CloudFormation, or Ansible
Strong command of containerization and orchestration tools such as Docker and Kubernetes
Practical experience using data processing frameworks like Apache Spark and Databricks
Programming skills in Python, along with familiarity with data manipulation and ML libraries such as Pandas, TensorFlow, and PyTorch
Knowledge of CI/CD tools such as Jenkins, GitLab CI/CD, and GitHub Actions
Hands-on experience with version control systems and MLOps platforms including Git, MLflow, and Kubeflow
Solid grasp of monitoring, logging, and alerting tools such as Prometheus and Grafana
Strong analytical and problem-solving capabilities, with the ability to perform well both independently and collaboratively
Effective communication and documentation skills
Nice to have
Experience with DataOps principles and tools such as Airflow and dbt
Understanding of data governance platforms like Collibra
Exposure to Big Data technologies such as Hadoop and Hive
Cloud platform or data engineering certifications

Work arrangement
No