Senior Systems Engineer - Data DevOps/MLOps

As a Senior Systems Engineer specializing in Data DevOps/MLOps, your role involves enhancing the team's capabilities by leveraging your expertise in data engineering, data pipeline automation, and machine learning model operationalization. You will be responsible for: - Developing, deploying, and managing Continuous Integration/Continuous Deployment (CI/CD) pipelines for data integration and machine learning model deployment - Setting up and sustaining infrastructure for data processing and model training using cloud-based resources and services - Automating processes for data validation, transformation, and workflow orchestration - Collaborating closely with data scientists, software engineers, and product teams to integrate ML models seamlessly into production - Enhancing model serving and monitoring to improve performance and reliability - Managing data versioning, lineage tracking, and ensuring the reproducibility of ML experiments - Actively seeking enhancements in deployment processes, scalability, and infrastructure resilience - Implementing stringent security protocols to safeguard data integrity and compliance with regulations - Troubleshooting and resolving issues throughout the data and ML pipeline lifecycle Qualifications required for this role include: - 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 the 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 This summary provides an overview of the responsibilities and qualifications expected from you as a Senior Systems Engineer with Data DevOps/MLOps expertise. As a Senior Systems Engineer specializing in Data DevOps/MLOps, your role involves enhancing the team's capabilities by leveraging your expertise in data engineering, data pipeline automation, and machine learning model operationalization. You will be responsible for: - Developing, deploying, and managing Continuous Integration/Continuous Deployment (CI/CD) pipelines for data integration and machine learning model deployment - Setting up and sustaining infrastructure for data processing and model training using cloud-based resources and services - Automating processes for data validation, transformation, and workflow orchestration - Collaborating closely with data scientists, software engineers, and product teams to integrate ML models seamlessly into production - Enhancing model serving and monitoring to improve performance and reliability - Managing data versioning, lineage tracking, and ensuring the reproducibility of ML experiments - Actively seeking enhancements in deployment processes, scalability, and infrastructure resilience - Implementing stringent security protocols to safeguard data integrity and compliance with regulations - Troubleshooting and resolving issues throughout the data and ML pipeline lifecycle Qualifications required for this role include: - 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-solv