AI/ML Engineer

Vantor Inc.
McLean, VA, United States
8 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Compensation
$150,000.0 - $200,000.0
Working hours
Regular working hours
Job source

Tech stack

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
+2 more
Software Version Control Recurrent Neural Networks

Job 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

Requirements

  • 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

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