Lead Systems Engineer - Data DevOps/MLOps

Lead Systems Engineer

Responsibilities

• Build, launch, and oversee CI/CD pipelines that enable smooth data integration and ML model deployment
• Build dependable infrastructure to support processing, training, and serving of machine learning models through cloud-based platforms
• Streamline key processes like data validation, transformation, and orchestration to enhance operational efficiency
• Partner with cross-functional teams, such as data scientists and engineers, to embed ML solutions within production systems
• Enhance model serving, performance tracking, and reliability within production environments
• Maintain data versioning, lineage tracking, and reproducibility throughout ML experiments and workflows
• Discover and apply strategies to boost the scalability, efficiency, and resilience of infrastructure systems
• Apply strict security protocols to protect data and maintain compliance with applicable regulations
• Troubleshoot and fix technical problems within data pipelines and ML deployment processes

Requirements

• Bachelor's or Master's degree in Computer Science, Data Engineering, or similar field
• 8+ years of professional background in Data DevOps, MLOps, or comparable areas
• Strong command of cloud platforms including Azure, AWS, or GCP
• Capability with Infrastructure as Code tools such as Terraform, CloudFormation, or Ansible
• Competency in containerization and orchestration solutions like Docker and Kubernetes
• Practical experience using data processing frameworks such as Apache Spark and Databricks
• Strong Python skills along with familiarity with libraries like Pandas, TensorFlow, and PyTorch
• Familiarity with CI/CD tools including Jenkins, GitLab CI/CD, and GitHub Actions
• Background using version control systems and MLOps platforms such as Git, MLflow, and Kubeflow
• Working knowledge of monitoring and alerting tools like Prometheus and Grafana
• Solid problem-solving skills paired with the ability to make independent decisions
• Strong communication abilities and technical documentation expertise

Nice to have

• Experience with DataOps approaches and tools like Airflow or dbt
• Familiarity with data governance platforms such as Collibra
• Exposure to Big Data technologies including Hadoop or Hive
• Certifications demonstrating expertise in cloud platforms or data engineering tools