Senior Systems Engineer - Data DevOps/MLOps

Senior Systems Engineer

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