Senior Systems Engineer - Data DevOps/MLOps

We're seeking a motivated and detail-oriented Senior 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

Work arrangement
Hybrid