Senior Systems Engineer - Data DevOps/MLOps

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

The successful candidate will have in-depth knowledge of data engineering, data pipeline automation, and the integration of machine learning models into production systems. We're looking for a collaborative team player skilled at building, deploying, and managing scalable data and ML pipelines that support strategic goals.

Responsibilities

• Design CI/CD pipelines for data integration and machine learning model deployment
• Build and maintain cloud-based infrastructure for data processing and model training
• Automate data validation, transformation, and workflow orchestration processes
• Work closely with data scientists, software engineers, and product teams to deploy ML models into production
• Improve model serving and monitoring processes to boost performance and reliability
• Maintain data versioning, lineage tracking, and reproducibility across ML experiments
• Spot opportunities to improve deployment processes, scalability, and infrastructure resilience
• Apply security protocols to protect data integrity and ensure compliance
• Troubleshoot and resolve issues throughout the data and ML pipeline lifecycle

Requirements

• Bachelor's or Master's degree in Computer Science, Data Engineering, or a similar field
• At least 5 years of experience working in Data DevOps, MLOps, or a comparable role
• Hands-on experience with cloud platforms such as Azure, AWS, or GCP
• Experience using Infrastructure as Code (IaC) tools like Terraform, CloudFormation, or Ansible
• Strong skills in containerization and orchestration tools such as Docker and Kubernetes
• Experience working with data processing frameworks like Apache Spark or Databricks
• Strong Python skills, along with experience using data manipulation and ML libraries such as Pandas, TensorFlow, or PyTorch
• Experience with CI/CD tools such as Jenkins, GitLab CI/CD, or GitHub Actions
• Familiarity with version control systems like Git and MLOps platforms such as MLflow or Kubeflow
• Working knowledge of monitoring, logging, and alerting tools like Prometheus or Grafana
• Solid problem-solving skills and the ability to work both independently and as part of a team
• Strong communication and documentation abilities

Nice to have

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