Senior Systems Engineer Data DevOpsMLOps

We are looking for a dedicated and proficient Senior Systems Engineer with extensive Data DevOps/MLOps knowledge to enhance our team.

The ideal candidate should possess a comprehensive knowledge of data engineering data pipeline automation and machine learning model operationalization. The role demands a cooperative professional skilled in designing deploying and managing extensive data and ML pipelines in alignment with organizational objectives.

• Develop deploy and manage Continuous Integration/Continuous Deployment (CI/CD) pipelines for data integration and machine learning model deployment
• Set up and sustain infrastructure for data processing and model training through cloud-based resources and services
• Automate processes for data validation transformation and workflow orchestration
• Work closely with data scientists software engineers and product teams for a smooth integration of ML models into production
• Enhance model serving and monitoring to boost performance and dependability
• Manage data versioning lineage tracking and the reproducibility of ML experiments
• Actively search for enhancements in deployment processes scalability and infrastructure resilience
• Implement stringent security protocols to safeguard data integrity and compliance with regulations
• Troubleshoot and solve issues throughout the data and ML pipeline lifecycle

• Bachelors or Masters degree in Computer Science Data Engineering or a related field
• 5 years of experience in Data DevOps MLOps or similar roles
• Proficiency in cloud platforms such as Azure AWS or GCP
• Background in Infrastructure as Code (IaC) tools like Terraform CloudFormation or Ansible
• Expertise in containerization and orchestration technologies including Docker and Kubernetes
• Hands-on experience with data processing frameworks such as Apache Spark and Databricks
• Proficiency in programming languages including Python with an understanding of data manipulation and ML libraries like Pandas TensorFlow and PyTorch
• Familiarity with CI/CD tools including Jenkins GitLab CI/CD and GitHub Actions
• Experience with version control tools and MLOps platforms such as Git MLflow and Kubeflow
• Strong understanding of monitoring logging and alerting systems including Prometheus and Grafana
• Excellent problem-solving abilities with capability to work independently and in teams
• Strong skills in communication and documentation

• Background in DataOps concepts and tools such as Airflow and dbt
• Knowledge of data governance platforms like Collibra
• Familiarity with Big Data technologies including Hadoop and Hive
• Certifications in cloud platforms or data engineering