Applied AI Scientist

Vantor Inc.
Myrtle Point, OR, United States
8 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Compensation
$135,000.0 - $180,000.0
Working hours
Regular working hours
Job source

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Amazon Web Services Data Analysis Computer Vision Microsoft Azure Cloud Computing Information Engineering Monitoring of Systems Python (Programming Language) Machine Learning SQL Databases
+4 more
Feature Engineering Deep Learning Machine Learning Operations Api Design

Job description

Vantor is seeking an Applied AI Scientist to design, build, and deploy data-driven solutions across modern web and cloud platforms. You will develop and productionize machine learning models, build AI-powered features, and collaborate with engineers, product teams, and clients to solve real business problems. Responsibilities include data exploration, feature engineering, model experimentation, evaluation, and MLOps practices for scalable, reliable delivery. You’ll work in agile teams, contribute to technical design, mentor peers, and continuously refine solutions based on outcomes and measurable client impact., * Design, train, and evaluate machine learning models for client solutions.

  • Translate business requirements into data and AI solution designs.
  • Perform data exploration, cleaning, and feature engineering.
  • Build and maintain ML pipelines and MLOps workflows in the cloud.
  • Collaborate with engineers to integrate models into production systems and APIs.
  • Monitor, test, and improve model performance and reliability over time.
  • Contribute to solution architecture and technical design discussions.
  • Work in agile teams, providing clear communication to stakeholders.
  • Document models, experiments, and deployment processes.
  • Mentor peers and contribute to best practices for AI at Vantor.

Requirements

  • Python
  • Machine learning
  • Deep learning
  • NLP or computer vision
  • Data engineering
  • MLOps/ML pipelines
  • Cloud platforms (AWS/Azure/GCP)
  • SQL and No
  • SQL databases
  • Model monitoring and evaluation
  • API development

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