Senior Systems Engineer - Data DevOps/MLOps

Job description

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. 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