Overview
The MLOps Data Engineer plays a crucial role in designing, implementing, and maintaining the data infrastructure and pipelines necessary for machine learning operations. They are responsible for developing, deploying, and monitoring the data processing and machine learning models in production, ensuring scalability, reliability, and efficiency.
Key Responsibilities
- Design and develop scalable data pipelines for machine learning models.
- Implement data processing and storage solutions for large-scale data sets.
- Collaborate with data scientists to integrate machine learning models into production environments.
- Automate model deployment, monitoring, and management processes.
- Optimize data infrastructure for performance and cost-efficiency.
- Ensure data quality and reliability in machine learning workflows.
- Implement version control and continuous integration for machine learning code and models.
- Work closely with DevOps and IT teams to ensure seamless integration of machine learning systems with existing infrastructure.
- Develop and maintain documentation for data pipelines, infrastructure, and processes.
- Participate in troubleshooting and performance optimization of machine learning systems.
- Stay updated with the latest advancements in data engineering, machine learning, and DevOps technologies.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, or related field.
- Proven experience in data engineering, data processing, and ETL pipelines.
- Proficiency in programming languages such as Python, Java, or Scala.
- Strong understanding of machine learning principles and algorithms.
- Experience with cloud platforms such as AWS, Azure, or Google Cloud.
- Knowledge of DevOps practices and tools for automation and infrastructure as code.
- Familiarity with containerization technologies like Docker and Kubernetes.
- Ability to collaborate effectively with cross-functional teams including data scientists, software developers, and IT operations.
- Excellent problem-solving and analytical skills.
- Strong communication and documentation abilities.
Skills: java,analytical skills,machine learning models,documentation,data,cloud platforms,scala,machine learning principles,data processing,containerization technologies,problem-solving,communication,python,etl pipelines,data engineering,devops practices,machine learning,devops
