Lead Systems Engineer - Data DevOps/MLOps

We are looking for a talented and dedicated Lead Systems Engineer who brings Data DevOps/MLOps experience to enhance innovation and efficiency throughout our data and machine learning operations.
Responsibilities
Build, launch, and oversee CI/CD pipelines that enable smooth data integration and ML model deployment
Build dependable infrastructure to support processing, training, and serving of machine learning models through cloud-based platforms
Streamline key processes like data validation, transformation, and orchestration to enhance operational efficiency
Partner with cross-functional teams, such as data scientists and engineers, to embed ML solutions within production systems
Enhance model serving, performance tracking, and reliability within production environments
Maintain data versioning, lineage tracking, and reproducibility throughout ML experiments and workflows
Discover and apply strategies to boost the scalability, efficiency, and resilience of infrastructure systems
Apply strict security protocols to protect data and maintain compliance with applicable regulations
Troubleshoot and fix technical problems within data pipelines and ML deployment processes
Requirements
Bachelor's or Master's degree in Computer Science, Data Engineering, or similar field
8+ years of professional background in Data DevOps, MLOps, or comparable areas
Strong command of cloud platforms including Azure, AWS, or GCP
Capability with Infrastructure as Code tools such as Terraform, CloudFormation, or Ansible
Competency in containerization and orchestration solutions like Docker and Kubernetes
Practical experience using data processing frameworks such as Apache Spark and Databricks
Strong Python skills along with familiarity with libraries like Pandas, TensorFlow, and PyTorch
Familiarity with CI/CD tools including Jenkins, GitLab CI/CD, and GitHub Actions
Background using version control systems and MLOps platforms such as Git, MLflow, and Kubeflow
Working knowledge of monitoring and alerting tools like Prometheus and Grafana
Solid problem-solving skills paired with the ability to make independent decisions
Strong communication abilities and technical documentation expertise
Nice to have
Experience with DataOps approaches and tools like Airflow or dbt
Familiarity with data governance platforms such as Collibra
Exposure to Big Data technologies including Hadoop or Hive
Certifications demonstrating expertise in cloud platforms or data engineering tools

Work arrangement
No