Lead Systems Engineer - Data DevOps/MLOps

Job description

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