Senior Systems Engineer - Data DevOps/MLOps

We're seeking a motivated, detail-oriented Senior 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.

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 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 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