Job Description

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

Responsibilities

Build, launch, and oversee CI/CD pipelines that enable smooth data integration and ML model rollout
Create a strong infrastructure foundation for training, processing, and serving machine learning models through cloud-based platforms
Streamline operations by automating essential workflows like data transformation, validation, and orchestration
Partner with cross-functional teams such as data engineers and scientists to bring ML solutions into live production environments
Enhance reliability, performance monitoring, and model serving within production systems
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 and maintain regulatory compliance
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 area
Over 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, CloudFormation, or Terraform
Capability in 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 PyTorch, TensorFlow, and Pandas
Familiarity with CI/CD tools including GitHub Actions, GitLab CI/CD, and Jenkins
Working knowledge of MLOps platforms and version control systems such as Kubeflow, MLflow, and Git
Awareness of alerting and monitoring tools such as Grafana and Prometheus
Solid ability to solve problems and make decisions independently
Strong skills in technical documentation and communication

Nice to have

Experience with DataOps tools and methodologies such as dbt or Airflow
Familiarity with data governance platforms such as Collibra
Exposure to Big Data technologies like Hive or Hadoop
Certifications demonstrating expertise in data engineering tools or cloud platforms