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# Machine Learning Engineer

- **Company:** Electronic Transaction Consultants, LLC
- **Location:** Frisco, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Vision, Software as a Service, Machine Learning, Language Modeling, Object Detection, OpenCV, Performance Tuning, Software Deployment, Cloud Platform System, Large Language Models, Deep Learning, Generative AI, Kubernetes, Information Technology, Machine Learning Operations, Docker
- **Published:** July 17, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=5ea0253eb2d8c197

## About the Role

* 5+ years of hands-on machine learning experience, with deep specialization in computer vision and a proven track record of shipping models to production.
 * Master's degree required (Ph.D. preferred) in Computer Science, Machine Learning, or a closely related field.
 * Extensive knowledge of computer vision architectures such as Vision Transformers and VLMs along with OpenCV and PIL.
 * Experience with MLOps tools (MLflow, Kubeflow, Docker, Kubernetes) able to own the full model lifecycle from experimentation through production monitoring.
 * Experience building and deploying LLM-based systems and Retrieval-Augmented Generation (RAG) pipelines, including vector store integration and retrieval evaluation.
 * Strong communicator who can translate complex research findings into actionable decisions for engineering and product stakeholders.

## Description

As part of our AI team, you'll collaborate closely with engineering teams to deliver high-impact features for our growing SaaS platform. The ideal candidate brings hands-on experience deploying computer vision and language models in production and applying MLOps best practices on cloud platforms.
Responsibilities:

 * Fine-tune and deploy computer vision and deep learning models for object detection, object tracking, and OCR at scale.
 * Develop vision-language models and Mixture of Experts architectures, from experimental design through production deployment.
 * Architect Retrieval-Augmented Generation (RAG) systems, including vector store design, hybrid search strategies, chunking pipelines, and context relevance evaluation.
 * Apply MLOps best practices for training, evaluation, deployment, and monitoring of production grade computer vision models, with an emphasis on clean, modular, maintainable code.
 * Contribute to our machine learning repositories and optimize models for performance, scalability, and real-time inference across edge and cloud environments.
 * Drive performance optimization and scalability of ML systems across edge and cloud environments.
 * Collaborate with cross-functional teams to integrate computer vision solutions into end-to-end products, translating research outcomes into measurable platform impact.


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