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.

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 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 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