Senior Machine Learning Engineer - Remote- W2 - COntract

Position- Senior Machine Learning Engineer –

Location-Remote

Contract - W2 

Min 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.