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

Work arrangement
No