Senior Systems Engineer - Data DevOps/MLOps

We're seeking a motivated, detail-oriented Senior Systems Engineer specializing in Data DevOps/MLOps to join our team. The right candidate will have in-depth knowledge of data engineering, data pipeline automation, and the integration of machine learning models into live environments. This position suits a collaborative individual skilled at creating, launching, and overseeing scalable data and ML pipelines that support strategic goals.

Responsibilities

Design CI/CD pipelines for data integration and machine learning model deployment Deploy and maintain cloud-based infrastructure for data processing and model training Automate workflows such as data validation, transformation, and orchestration Collaborate with data scientists, software engineers, and product teams to bring ML models into production Improve performance and reliability through optimized model serving and monitoring Maintain data versioning, lineage tracking, and reproducibility throughout ML experiments Pinpoint opportunities to strengthen deployment processes, scalability, and infrastructure resilience Apply security protocols to protect data integrity and ensure compliance Troubleshoot and resolve issues across the 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 similar roles Hands-on experience with cloud platforms such as Azure, AWS, or GCP Working knowledge of Infrastructure as Code (IaC) tools like Terraform, Cloud.

Formation, or Ansible Strong command of containerization and orchestration tools such as Docker and Kubernetes Experience with data processing frameworks like Apache Spark or Databricks Solid Python skills, including familiarity with data and ML libraries such as Pandas, Tensor. Flow, or Py. Torch Exposure to CI/CD tools like Jenkins, GitLab CI/CD, or GitHub Actions Understanding of version control systems like Git and MLOps platforms such as MLflow or Kubeflow Familiarity with monitoring, logging, and alerting tools like Prometheus or Grafana Sharp problem-solving skills with the ability to thrive independently or as part of a team Strong communication and documentation abilities Nice to have Exposure to Data.

Ops practices and tools such as Airflow or dbt Awareness of data governance frameworks and tools like Collibra Familiarity with Big Data technologies such as Hadoop or Hive Certifications in cloud platforms or data engineering disciplines