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
• Set up a reliable infrastructure to process, train, and serve machine learning models through cloud platforms
• Streamline essential workflows including data validation, transformation, and orchestration through automation
• Partner with cross-functional teams such as data scientists and engineers to bring ML solutions into live production environments
• Enhance model serving capabilities, performance tracking, and dependability 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 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 related discipline
• At least 8 years of experience working in Data DevOps, MLOps, or similar fields
• Strong command of cloud platforms including Azure, AWS, or GCP
• Proficiency with Infrastructure as Code tools such as Terraform, CloudFormation, or Ansible
• Solid grasp of containerization and orchestration tools 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 working with version control systems and MLOps platforms like Git, MLflow, and Kubeflow
• Working knowledge of monitoring and alerting tools such as Prometheus and Grafana
• Solid problem-solving skills paired with the ability to make independent decisions
• Clear communication abilities and strong technical documentation skills

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 related to cloud platforms or data engineering tools