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, Cloud.

Formation, 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, Tensor. Flow, or Py. Torch 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 Data.

Ops 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