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to shivaji D
Hi Professionals,
Hope you are doing great.
Please find the requirements below and send me matched resumes to shi...@nexwaveinc.com
Role: Lead Machine Learning Engineer – Computer Vision + Deep
Learning
Location: Remote
Exp Req : 12+
Key Responsibilities:
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.