Lead Systems Engineer - Data DevOps/MLOps

We're looking for a talented and dedicatedLead Systems Engineer who brings Data DevOps/MLOps expertise to help us boost innovation and efficiency throughout our data and machine learning operations.

Responsibilities

• Build, launch, and maintain CI/CD pipelines that enable smooth data integration and ML model deployment
• Create a solid infrastructure foundation for processing, training, and serving machine learning models through cloud-based platforms
• Streamline essential workflows by automating data validation, transformation, and orchestration tasks
• Partner with cross-functional teams, including data scientists and engineers, to bring ML solutions into production environments
• Enhance model serving capabilities, performance monitoring, and overall reliability within production systems
• Maintain data versioning, lineage tracking, and reproducibility throughout ML experiments and workflows
• Spot and act on opportunities to boost scalability, efficiency, and resilience across the infrastructure
• 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 a comparable field
• At least 8 years of hands-on experience in Data DevOps, MLOps, or similar areas
• Strong command of cloud platforms including Azure, AWS, or GCP
• Proficiency with Infrastructure as Code tools such as Terraform, CloudFormation, or Ansible
• Solid skills in containerization and orchestration tools like Docker and Kubernetes
• Practical experience working with data processing frameworks such as Apache Spark and Databricks
• Strong Python skills along with familiarity with libraries like Pandas, TensorFlow, and PyTorch
• Working knowledge of 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
• Familiarity with monitoring and alerting tools like Prometheus and Grafana
• Solid problem-solving abilities paired with independent decision-making skills
• Strong communication skills along with the ability to produce clear technical documentation

Nice to have

• Experience with DataOps methodologies and tools such as Airflow or dbt
• Familiarity with data governance platforms like Collibra
• Exposure to Big Data technologies such as Hadoop or Hive
• Relevant certifications in cloud platforms or data engineering tools