Senior Systems Engineer - Data DevOps/MLOps

Our team is seeking a skilled and committed Senior Systems Engineer with deep expertise in Data DevOps/MLOps to join our organization.
The successful applicant should have thorough understanding of data engineering, automated data pipelines, and deployment of machine learning models in production. This position requires a collaborative individual capable of architecting, implementing, and overseeing large-scale data and ML pipelines that support company goals.
Responsibilities
Build, launch, and oversee CI/CD pipelines supporting data integration and ML model rollout
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 performance and reliability
Oversee data versioning, lineage tracking, and reproducibility of 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 related discipline
Minimum 5 years of experience in Data DevOps, MLOps, or comparable positions
Skilled in cloud platforms such as Azure, AWS, or GCP
Experienced with Infrastructure as Code tools like Terraform, CloudFormation, or Ansible
Strong knowledge of containerization and orchestration tools, including Docker and Kubernetes
Practical experience with data processing frameworks such as Apache Spark and Databricks
Skilled in programming languages like Python, with familiarity in data manipulation and ML libraries such as Pandas, TensorFlow, and PyTorch
Knowledgeable in CI/CD tools such as Jenkins, GitLab CI/CD, and GitHub Actions
Experienced with version control systems and MLOps platforms including Git, MLflow, and Kubeflow
Solid grasp of monitoring, logging, and alerting tools such as Prometheus and Grafana
Strong problem-solving skills with the ability to perform well both independently and collaboratively
Excellent communication and documentation abilities
Nice to have
Experience with DataOps principles and tools like Airflow and dbt
Understanding of data governance platforms such as Collibra
Exposure to Big Data technologies including Hadoop and Hive
Cloud or data engineering certifications

Work arrangement
No