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
