Lead Systems Engineer - Data DevOps/MLOps

We are seeking a skilled and passionate Lead Systems Engineer with Data DevOps/MLOps expertise to drive innovation and efficiency across our data and machine learning operations.

Responsibilities

• Design, deploy, and manage CI/CD pipelines for seamless data integration and ML model deployment
• Establish robust infrastructure for processing, training, and serving machine learning models using cloud-based solutions
• Automate critical workflows such as data validation, transformation, and orchestration for streamlined operations
• Collaborate with cross-functional teams, including data scientists and engineers, to integrate ML solutions into production environments
• Improve model serving, performance monitoring, and reliability in production ecosystems
• Ensure data versioning, lineage tracking, and reproducibility across ML experiments and workflows
• Identify and implement opportunities to improve scalability, efficiency, and resilience of the infrastructure
• Enforce rigorous security measures to safeguard data and ensure compliance with relevant regulations
• Debug and resolve technical issues in data pipelines and ML deployment workflows

Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field
• 8+ years of experience in Data DevOps, MLOps, or related disciplines
• Expertise in cloud platforms such as Azure, AWS, or GCP
• Skills in Infrastructure as Code tools like Terraform, CloudFormation, or Ansible
• Proficiency in containerization and orchestration technologies such as Docker and Kubernetes
• Hands-on experience with data processing frameworks including Apache Spark and Databricks
• Proficiency in Python with familiarity with libraries including Pandas, TensorFlow, and PyTorch
• Knowledge of CI/CD tools such as Jenkins, GitLab CI/CD, and GitHub Actions
• Experience with version control systems and MLOps platforms including Git, MLflow, and Kubeflow
• Understanding of monitoring and alerting tools like Prometheus and Grafana
• Strong problem-solving and independent decision-making capabilities
• Effective communication and technical documentation skills

Nice to have

• Background in DataOps methodologies and tools such as Airflow or dbt
• Knowledge of data governance platforms like Collibra
• Familiarity with Big Data technologies such as Hadoop or Hive
• Showcase of certifications in cloud platforms or data engineering tools

Benefits

Opportunity to work on technical challenges that may impact across geographies

Vast opportunities for self-development: online university, knowledge sharing opportunities globally, learning opportunities through external certifications