Senior Systems Engineer - Data DevOps/MLOps

Senior Systems Engineer

The successful applicant should have thorough understanding of data engineering, automated data pipelines, and deployment of machine learning models in production. This position requires a collaborative individual capable of architecting, implementing, and overseeing large-scale data and ML pipelines that support company 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
• 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 reproducibility of ML experiments
• Continuously identify opportunities to improve deployment workflows, scalability, and infrastructure resilience
• Enforce robust security measures to protect data integrity and ensure regulatory compliance
• 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 experience in Data DevOps, MLOps, or comparable positions
• Skilled in cloud platforms such as Azure, AWS, or GCP
• Experienced with Infrastructure as Code tools like Terraform, CloudFormation, or Ansible
• Strong knowledge of containerization and orchestration tools, including Docker and Kubernetes
• Practical experience with data processing frameworks such as Apache Spark and Databricks
• Skilled in programming languages like Python, with familiarity in data manipulation and ML libraries such as Pandas, TensorFlow, and PyTorch
• Knowledgeable in CI/CD tools such as Jenkins, GitLab CI/CD, and GitHub Actions
• Experienced 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 problem-solving skills with the ability to perform well both independently and collaboratively
• Excellent communication and documentation abilities

Nice to have

• Experience with DataOps principles and tools like Airflow and dbt
• Understanding of data governance platforms such as Collibra
• Exposure to Big Data technologies including Hadoop and Hive
• Cloud or data engineering certifications