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AI Data and MLOps Engineer
4 weeks ago
Position Summary
SGK seeks an AI Data and MLOps Engineer to join our Production IT team and AI Lab. Directed by the Director of Digital Content Strategic Solutions A.I. & Cloud Governance, you'll work on innovative projects at the intersection of AI, Data Science, MLOps, and Cloud technologies. You'll design, develop, and maintain data pipelines and infrastructure to support AI-powered solutions on AWS Cloud environments. Your role will involve implementing MLOps practices, automating model deployment, and setting up monitoring systems to ensure seamless integration and performance of AI models and applications.
Key Responsibilities
Design and implement scalable data pipelines on AWS for ingesting, transforming, and storing large volumes of data
Develop data warehouses, data lakes, and other storage solutions on AWS, ensuring data quality, security, and accessibility
Collaborate with AI developers and data scientists to understand and meet data requirements for AI models
Implement data governance and management best practices, ensuring compliance with privacy and security regulations
Optimize data pipelines and infrastructure for performance, cost-effectiveness, and reliability
Monitor and troubleshoot data pipelines, ensuring high availability and minimal downtime
Design and implement CI/CD pipelines for automated model training, testing, and deployment
Automate model deployment and scaling using containerization (Docker) and orchestration tools (Kubernetes)
Develop and implement monitoring systems to track AI model performance and manage model drift
Use infrastructure automation tools (Terraform, AWS CloudFormation) for cloud resource management
Implement model versioning and experiment tracking (MLflow, DVC) for traceability and reproducibility
Collaborate cross-functionally to integrate AI models into existing applications and services
Stay updated with the latest advancements in data engineering, AI, and cloud technologies
Qualifications
Bachelor's or Master's in Computer Science, Data Science, or related field
Proven experience in data engineering, focusing on AWS data pipelines and infrastructure
Strong proficiency in Python, Java, or Scala; experience with Apache Spark and Hadoop
Expertise in AWS data services (S3, Glue, Athena, Redshift, EMR)
Knowledge of data warehousing, modeling, and management best practices
Experience with data streaming technologies (Apache Kafka, AWS Kinesis)
Familiarity with AI and machine learning concepts and frameworks (TensorFlow, PyTorch, scikit-learn)
Experience with CI/CD tools, Docker, Kubernetes, and AWS CloudFormation
Knowledge of model monitoring tools (Prometheus, Grafana) and MLOps frameworks (Kubeflow, MLflow, AWS SageMaker)
Understanding of security best practices for data and AI model deployment
Strong problem-solving, analytical, and communication skills
