Job Requirements

Summary

We're 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 monitoringArchitect 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 devicesDevelop vector embedding pipelines for visual content using AWS services and deep learning frameworksCreate APIs for seamless integration with third-party annotation services and automated dataset creationImplement active learning pipelines that intelligently select high-value images for annotation, optimizing annotation ROIBuild data quality and validation frameworks to ensure consistency across the annotation lifecycleDevelop infrastructure automation using AWS CloudFormation/CDK for scalable deep learning workflowsEstablish monitoring systems for data drift, annotation quality, and model performanceCreate skeleton frameworks and templates enabling customers to deploy their own deep learning modelsOptimize storage and retrieval mechanisms for large-scale image repositories

Work Experience

Requirements:

Bachelor's or Master's degree in Computer Science, Engineering, or related field5+ years of experience in MLOps or ML Engineering with focus on computer vision applicationsExperience building data lakes or large-scale data repositories for unstructured dataStrong understanding of vector databases, embedding models, and similarity search algorithmsHands-on experience with AWS services (S3, SageMaker, Lambda, Step Functions, Glue)Proficiency in Python and experience with PyTorch or TensorFlowExperience implementing active learning systems for optimizing annotation workflowsKnowledge of RESTful API design and integration with third-party servicesFamiliarity with annotation tools and workflows for computer vision datasetsExperience with containerization (Docker) and orchestration (Kubernetes/EKS)Understanding of data governance and security best practices for sensitive image data