AI Engineer (Mid-Senior): ML, DRL & MLOps

nou Systems, Inc.; Colorado Springs (CO); On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Competitive Wages

Medical, Rx, Dental & Vision Insurance

Health Savings Account eligibility

Life, Short-Term & Long-Term Disabiliy

Paid Time Off

401(k) Retirement Plan

Employee Stock Ownership Plan (ESOP)

Flexible Spending Accounts

Tuition Reimbursement

Personal Development & Lunch-n-Learns

Corporate Sponsored Events & Community

Job summary

nou Systems, Inc. is seeking an AI Engineer to join our Technical Solutions division to design, train, and deploy AI/ML systems for defense-focused applications.

You will work on DRL analytics, explainability, adversarial ML, and model interpretability tools, collaborating with data scientists and educating non-data-science teams on AI value and limitations. You will lead AI projects, develop training data sets, and build robust MLOps pipelines while communicating results clearly in documents

Qualifications

• Master's degree in computer science, math, physics, or related field (or equivalent).
• Active security clearance or ability to obtain one.
• Extensive hands-on experience with deep learning models.
• Strong software development fundamentals in Python.

Responsibilities

• Architect AI/ML solutions and tradeoffs.
• Develop, train, and analyze AI/ML systems (DRL, XAI, adversarial ML).
• Build and maintain MLOps pipelines.
• Mentor junior engineers and collaborate with teams.
• Present results to non-specialist audiences.

Skills

Python

AI/ML development

Team leadership

Communication skills

MLOps

Education

Master's degree in CS/Math/Physics

Bachelor's degree + 2 years experience

Tools

PyTorch

TensorFlow

Jax

PyTorch Lightning

MLflow

Git/GitLab

Docker

Job description

nou Systems, Inc. is seeking an AI Engineer to join our Technical Solutions division to design, train, and deploy AI/ML systems for defense-focused applications.

You will work on DRL analytics, explainability, adversarial ML, and model interpretability tools, collaborating with data scientists and educating non-data-science teams on AI value and limitations. You will lead AI projects, develop training data sets, and build robust MLOps pipelines while communicating results clearly in documents