Senior Systems Engineer - Data DevOps/MLOps

Senior Systems Engineer

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.

Responsibilities

• 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

Requirements

• Bachelor’s or Master’s 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

Nice to have

• 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