Senior Systems Engineer - Data DevOps/MLOps

Systems Engineer

The right candidate will bring strong knowledge of data engineering, pipeline automation, and the operationalization of machine learning models. This position calls for a collaborative professional capable of architecting, implementing, and maintaining large-scale data and ML pipelines that support the company's broader goals.

Responsibilities

• Build, launch, and maintain CI/CD pipelines supporting data integration and ML model deployment
• 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 system performance and reliability
• Oversee data versioning, lineage tracking, and reproducibility across 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 a related discipline
• At least 5 years of experience in Data DevOps, MLOps, or comparable positions
• Strong command of cloud platforms such as Azure, AWS, or GCP
• Experience using Infrastructure as Code (IaC) tools like Terraform, CloudFormation, or Ansible
• Skilled in containerization and orchestration tools, including Docker and Kubernetes
• Practical experience with data processing frameworks like Apache Spark and Databricks
• Strong programming skills in Python, along with familiarity in data manipulation and ML libraries such as Pandas, TensorFlow, and PyTorch
• Working knowledge of CI/CD tools, including Jenkins, GitLab CI/CD, and GitHub Actions
• Hands-on experience with version control systems and MLOps platforms such as Git, MLflow, and Kubeflow
• Solid understanding of monitoring, logging, and alerting tools like Prometheus and Grafana
• Strong analytical and problem-solving skills, with the ability to work both independently and collaboratively
• Excellent communication and documentation abilities

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
• Relevant certifications in cloud computing or data engineering