MLOps Engineer

Years of Experience

Role

Location

Summary

Responsibilities:
• Design and implement end-to-end computer vision ML training pipelines on AWS SageMaker for model training, validation, deployment, and monitoring • Architect and build a scalable image data lake solution enabling multi-modal search capabilities (structured metadata, image-to-image, text-to-image) along with data upload capability from edge devices • Develop vector embedding pipelines for visual content using AWS services and deep learning frameworks • Create APIs for seamless integration with third-party annotation services and automated dataset creation • Implement active learning pipelines that intelligently select high-value images for annotation, optimizing annotation ROI • Build data quality and validation frameworks to ensure consistency across the annotation lifecycle • Develop infrastructure automation using AWS CloudFormation/CDK for scalable deep learning workflows • Establish monitoring systems for data drift, annotation quality, and model performance • Create skeleton frameworks and templates enabling customers to deploy their own deep learning models • Optimize storage and retrieval mechanisms for large-scale image repositories Requirements: • Bachelor's or Master's degree in Computer Science, Engineering, or related field • 10+ years of experience in MLOps or ML Engineering with focus on computer vision applications • Experience building data lakes or large-scale data repositories for unstructured data • Strong understanding of vector databases, embedding models, and similarity search algorithms • Hands-on experience with AWS services (S3, SageMaker, Lambda, Step Functions, Glue) • Proficiency in Python and experience with PyTorch or TensorFlow • Experience implementing active learning systems for optimizing annotation workflows • Knowledge of RESTful API design and integration with third-party services • Familiarity with annotation tools and workflows for computer vision datasets • Experience with containerization (Docker) and orchestration (Kubernetes/EKS) • Understanding of data governance and security best practices for sensitive image data