Lead Systems Engineer - Data DevOps/MLOps

We are seeking a skilled Lead Systems Engineer with Data DevOps/MLOps expertise to drive innovation and efficiency across data and machine learning operations. You will design, deploy, and manage CI/CD pipelines for seamless data integration and ML model deployment, and establish robust cloud-based infrastructure for processing, training, and serving machine learning models.

Key responsibilities include automating critical workflows such as data validation, transformation, and orchestration, collaborating with data scientists and engineers to integrate ML solutions into production, and ensuring data versioning, lineage tracking, and reproducibility across ML experiments. You will also improve model serving, performance monitoring, and reliability in production ecosystems.

The role requires enforcing rigorous security measures to safeguard data, identifying opportunities to improve scalability and resilience of infrastructure, and debugging technical issues in data pipelines and ML deployment workflows.

Candidates must have 8+ years of experience in Data DevOps, MLOps, or related fields, with expertise in cloud platforms (Azure, AWS, or GCP), IaC tools (Terraform, CloudFormation, Ansible), containerization (Docker, Kubernetes), data processing frameworks (Apache Spark, Databricks), Python, and CI/CD tools (Jenkins, GitLab CI/CD, GitHub Actions). Experience with MLflow, Kubeflow, Prometheus, and Grafana is also expected.