Position- Senior Deep Learning Engineer Location-Remote Contract Experience 10+ years of industry experience in Machine Learning, Deep Learning, and Computer Vision with a proven track record of designing, building, and deploying production-grade AI solutions at scale.

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.

Required Technical Skills Machine Learning

• Strong understanding of supervised and unsupervised learning algorithms.
• Hands-on experience with regression, classification, clustering, ensemble learning, decision trees, random forests, and gradient boosting algorithms such as XGBoost, LightGBM, and CatBoost.
• Strong foundation in probability, statistics, hypothesis testing, experimental design, and statistical inference.
• Experience with feature engineering, model evaluation, cross-validation, bias-variance analysis, model calibration, and hyperparameter optimization. Deep Learning & Computer Vision - Strong expertise in TensorFlow (mandatory).
• Extensive experience training deep learning models from scratch on large-scale image datasets.
• Strong understanding of convolutional neural networks and modern computer vision architectures.
• Experience with image classification, object detection, semantic segmentation, instance segmentation, localization, embeddings, feature extraction, and image similarity.
• Experience designing custom neural network architectures, optimization techniques, loss functions, and distributed deep learning workflows. Production Machine Learning (Mandatory)
• Proven experience designing, deploying, and operating production machine learning systems.
• Experience building scalable machine learning training and inference pipelines.
• Experience with model registry, experiment tracking, model versioning, CI/CD for machine learning, and model monitoring.
• Experience designing data ingestion pipelines and managing large-scale datasets.
• Hands-on experience with distributed training and multi-GPU environments.
• Strong understanding of machine learning system scalability, performance optimization, and production reliability. Programming & Software Engineering
• Expert-level Python programming skills with experience building production-quality machine learning applications, reusable libraries, and scalable data processing pipelines.
• Strong understanding of software engineering principles, object-oriented design, design patterns, testing, debugging, performance optimization, and version control.
• Experience writing modular, maintainable, and production-quality code.
• Strong SQL and data analysis skills. Cloud & Infrastructure
• Hands-on experience with AWS SageMaker.
• Experience deploying machine learning workloads on AWS.
• Familiarity with cloud-native machine learning infrastructure and scalable training environments.

Preferred Qualifications

• Experience in automobile insurance, collision repair, automotive AI, or related computer vision domains.
• Experience building enterprise-scale machine learning products serving production customers.
• Exposure to MLOps platforms, distributed computing, and large-scale machine learning infrastructure.
• Master''s or PhD in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, or a related quantitative discipline.