Senior Systems Engineer - Data DevOps/MLOps

We're seeking a skilled Senior Systems Engineer with deep expertise in Data DevOps/MLOps to join our growing team. 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