Sr. Machine Learning Engineer Computer Vision

Requirement details:

Role: Sr. Machine Learning Engineer Computer Vision

Location- Remote

Duration: 12+ Months

Job Description:

12+ Years of Experience

Design, develop, train, evaluate, and deploy production-grade machine learning and deep learning models for computer vision applications.

Build end-to-end machine learning pipelines covering data ingestion, preprocessing, feature engineering, model training, evaluation, deployment, monitoring, and continuous improvement.

Train deep neural networks from scratch on large-scale image datasets and optimize model architectures for accuracy, latency, scalability, and robustness.

Develop computer vision solutions for image classification, object detection, segmentation, localization, image similarity, and feature extraction.

Own the complete machine learning lifecycle, including experiment design, hyperparameter optimization, model versioning, model registry, reproducible training pipelines, and model performance monitoring.

Design and optimize distributed training pipelines utilizing multiple GPUs and efficiently process large-scale datasets.

Evaluate model performance using statistical methods, rigorous experimentation, and business-centric success metrics.

Apply model explainability techniques to validate, interpret, and communicate model predictions.

Build scalable training and inference pipelines using AWS SageMaker and other cloud-native services.

Collaborate closely with Product Managers, Data Scientists, Machine Learning Engineers, Software Engineers, Data Engineers, QA teams, domain experts, and business stakeholders to deliver production-ready AI solutions.

Drive continuous model improvements through hypothesis-driven experimentation, error analysis, performance optimization, and data-driven decision making.

Lead and mentor Machine Learning Engineers, Data Scientists, and Software Engineers.

Provide technical direction, establish engineering best practices, conduct architecture and code reviews, and drive execution of large-scale machine learning initiatives.