Senior Systems Engineer - Data DevOps/MLOps

We're seeking a motivated, detail-orientedSenior Systems Engineer who specializes in Data DevOps/MLOps to join our team.

The right candidate will have strong expertise in data engineering, pipeline automation, and embedding machine learning models into live operational systems. This position suits a collaborative individual skilled at creating, launching, and overseeing scalable data and ML pipelines that support organizational goals.

Responsibilities

• Build CI/CD pipelines to support data integration and ML model deployment
• Set up and manage cloud-based infrastructure for data processing and model training
• Streamline operations through automation of data validation, transformation, and workflow orchestration
• Partner with data scientists, software engineers, and product teams to bring ML models into production
• Boost reliability and performance through optimized model serving and monitoring
• Maintain data versioning, lineage tracking, and reproducibility throughout ML experiments
• Pinpoint opportunities to strengthen deployment workflows, scalability, and infrastructure resilience
• Apply security protocols to protect data integrity and uphold compliance standards
• Troubleshoot and resolve issues throughout the data and ML pipeline lifecycle

Requirements

• Bachelor's or Master's degree in Computer Science, Data Engineering, or related discipline
• At least 5 years of experience working in Data DevOps, MLOps, or similar roles
• Hands-on experience with cloud platforms including Azure, AWS, or GCP
• Working knowledge of Infrastructure as Code (IaC) tools such as Terraform, CloudFormation, or Ansible
• Strong command of containerization and orchestration solutions like Docker and Kubernetes
• Experience working with data processing frameworks such as Apache Spark or Databricks
• Solid Python skills, along with familiarity with ML and data libraries like Pandas, TensorFlow, or PyTorch
• Exposure to CI/CD tools such as Jenkins, GitLab CI/CD, or GitHub Actions
• Familiarity with Git and MLOps platforms including MLflow or Kubeflow
• Experience with monitoring, logging, and alerting tools like Prometheus or Grafana
• Strong analytical and problem-solving skills, with the ability to work solo or as part of a team
• Clear communication skills paired with strong documentation habits

Nice to have

• Exposure to DataOps methodologies and tools such as Airflow or dbt
• Awareness of data governance frameworks and platforms like Collibra
• Familiarity with Big Data technologies including Hadoop or Hive
• Certifications related to cloud platforms or data engineering