Lead Systems Engineer - Data DevOps/MLOps

We're looking for a talented and dedicated Lead Systems Engineer who brings Data DevOps/MLOps expertise to help us boost innovation and efficiency throughout our data and machine learning operations. Responsibilities Build, launch, and maintain CI/CD pipelines that enable smooth data integration and ML model deployment Create a solid infrastructure foundation for processing, training, and serving machine learning models through cloud-based platforms Streamline essential workflows by automating data validation, transformation, and orchestration tasks Partner with cross-functional teams, including data scientists and engineers, to bring ML solutions into production environments Enhance model serving capabilities, performance monitoring, and overall reliability within production systems Maintain data versioning, lineage tracking, and reproducibility throughout ML experiments and workflows Spot and act on opportunities to boost scalability, efficiency, and resilience across the infrastructure Apply strict security protocols to protect data and maintain compliance with applicable regulations Troubleshoot and fix technical problems within data pipelines and ML deployment processes Requirements Bachelor's or Master's degree in Computer Science, Data Engineering, or a comparable field At least 8 years of hands-on experience in Data DevOps, MLOps, or similar areas Strong command of cloud platforms including Azure, AWS, or GCP Proficiency with Infrastructure as Code tools such as Terraform, CloudFormation, or Ansible Solid skills in containerization and orchestration tools like Docker and Kubernetes Practical experience working with data processing frameworks such as Apache Spark and Databricks Strong Python skills along with familiarity with libraries like Pandas, TensorFlow, and PyTorch Working knowledge of CI/CD tools including Jenkins, GitLab CI/CD, and GitHub Actions Background using version control systems and MLOps platforms such as Git, MLflow, and Kubeflow Familiarity with monitoring and alerting tools like Prometheus and Grafana Solid problem-solving abilities paired with independent decision-making skills Strong communication skills along with the ability to produce clear technical documentation Nice to have Experience with DataOps methodologies and tools such as Airflow or dbt Familiarity with data governance platforms like Collibra Exposure to Big Data technologies such as Hadoop or Hive Relevant certifications in cloud platforms or data engineering tools