Machine Learning Engineer

Electronic Transaction Consultants, LLC
Frisco, TX, United States
about 2 months ago
Apply on www.indeed.com
Prepare application

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Working hours
Regular working hours
Job source

Tech stack

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
+5 more
Generative AI Kubernetes Information Technology Machine Learning Operations Docker

Job 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.

Requirements

  • 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.

Benefits & conditions

Pulled from the full job description

  • Referral program
  • Health insurance
  • Retirement plan
  • Dental insurance
  • Bereavement leave, We offer a Total Rewards plan designed with you and your family’s health and wellness in mind that includes:
  • Paid days off (i.e. vacation, sick days, bereavement leave)
  • Health and Dental plans
  • Retirement plans
  • Employee and Family Assistance Program (EFAP)
  • Employee referral program

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.indeed.com
Prepare application

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

3:14 min

Structuring career paths and localized data architectures

Ulrich Wurstbauer +1 · LIVE

2:28 min

Understanding Kubernetes architecture and core cluster components

Marc Nimmerrichter · World Congress 2022

4:53 min

Achieving real-time tracking performance with OpenCV and segmentation

Thomas Endres Thomas Endres +2 · World Congress 2021

2:07 min

Inspecting default bridge architectures and custom Docker networks

Oliver Seitz Oliver Seitz · World Congress 2025

2:03 min

Solving complex engineering challenges in artificial intelligence deployment

Nico Axtmann · World Congress 2022

4:04 min

Overview of Kubernetes operators and custom resource definitions

Philipp Krenn · World Congress 2022

Videos

See all

Related articles

See all