MLOps Engineer

Job Description

Summary Were seeking an experienced MLOps Engineer to build and maintain our computer vision infrastructure on AWS. The ideal candidate will develop model training pipeline, a comprehensive image data lake with advanced search capabilities, implement active learning pipelines for efficient annotation, and create frameworks enabling customers to deploy their own deep learning models. This role combines MLOps expertise with data engineering to create scalable, production-ready computer vision systems. 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 Work Experience Requirements: Bachelors or Masters degree in Computer Science, Engineering, or related field 5+ 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