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# AI/ML Engineer

- **Company:** Vantor Inc.
- **Location:** McLean, VA, United States
- **Experience:** Expert
- **Salary:** $150,000.0 - $200,000.0
- **Contract:** Permanent contract
- **Skills:** Computer-Aided Design, Artificial Intelligence, Amazon Web Services, Microsoft Azure, Continuous Integration, Information Engineering, Extract Transform Load (ETL), Python (Programming Language), Machine Learning, Deep Learning, Model Validation, Machine Learning Operations, Software Version Control, Recurrent Neural Networks
- **Published:** October 12, 2026
- **Apply:** https://find.jobs/jobs-near-me/apply/ats-redirect/?id=3017381508-2

## About the Role

* Machine learning algorithms
 * Deep learning (CNNs, RNNs, transformers)
 * Python
 * Tensor
 * Flow or Py
 * Torch
 * MLOps and CI/CD for MLCloud platforms (AWS/Azure/GCP)
 * Data engineering (ETL, pipelines)
 * Model evaluation and optimization
 * Version control (Git)
 * Secure, scalable software design

## Description

Vantor seeks a Senior AI/ML Engineer to advance AI capabilities in our Defense & Aerospace portfolio. You will architect, implement, and optimize machine learning and deep learning solutions within the Data Science & AI department. Responsibilities include building secure, scalable ML pipelines, integrating models into mission systems, and collaborating with cross-functional teams to solve complex operational challenges. You'll mentor peers, influence AI strategy, and help foster Vantor's culture of innovation, diversity, and continuous learning while delivering high-impact, production-grade AI services., * Design, build, and deploy ML and deep learning models for defense and aerospace applications

 * Collaborate with cross-functional teams to define AI requirements and translate mission needs into technical solutions
 * Develop secure, scalable ML pipelines and MLOps practices for production systems
 * Optimize model performance, reliability, and latency for real-world operational environments
 * Mentor junior engineers and contribute to Vantor's AI best practices and technical standards
 * Document architectures, experiments, and results for stakeholders and compliance needs