Lead Systems Engineer - Data DevOps/MLOps

We're looking for a talented and dedicated Lead Systems Engineer who brings Data DevOps/MLOps expertise to help drive innovation and efficiency throughout our data and machine learning operations.

Responsibilities

Build, launch, and maintain CI/CD pipelines that enable smooth data integration and ML model deployment Create a solid cloud-based infrastructure foundation for training, processing, and serving machine learning models Streamline operations by automating essential workflows like data validation, transformation, and orchestration Work alongside cross-functional teams, such as data scientists and engineers, to bring ML solutions into production Enhance performance monitoring, model serving, and reliability within production environments Maintain reproducibility, lineage tracking, and data versioning throughout ML workflows and experiments Spot and act on opportunities to boost the infrastructure's resilience, efficiency, and scalability Apply strict security protocols to protect data while maintaining compliance with applicable regulations Troubleshoot and fix technical problems within ML deployment workflows and data pipelines Requirements A Bachelor's or Master's degree in Data Engineering, Computer Science, or a related discipline At least 8 years working in MLOps, Data DevOps, or similar fields Strong command of cloud platforms including GCP, AWS, or Azure Proficiency with Infrastructure as Code tools such as Ansible, Cloud.

Formation, or Terraform Hands-on knowledge of orchestration and containerization technologies like Kubernetes and Docker Practical experience using data processing frameworks such as Databricks and Apache Spark Strong Python skills along with familiarity with libraries like Py. Torch, Tensor. Flow, and Pandas Familiarity with CI/CD tools including GitHub Actions, GitLab CI/CD, and Jenkins Experience working with MLOps platforms and version control systems such as Kubeflow, MLflow, and Git Working knowledge of alerting and monitoring tools such as Grafana and Prometheus Solid ability to solve problems and make decisions independently Strong technical documentation and communication abilities Nice to have Experience with Data.

Ops tools and methodologies such as dbt or Airflow Familiarity with data governance platforms like Collibra Exposure to Big Data technologies such as Hive or Hadoop Certifications related to data engineering tools or cloud platforms