AI Engineer

About This Role

This role involves building, deploying, and maintaining AI systems at scale, combining expertise from data science, software engineering, and systems engineering. The AI Engineer will help design and implement mission-critical AI solutions for intelligence collection, processing, and reporting.

Responsibilities

• Lead or contribute to cross-functional teams to develop and operationalize AI solutions for challenging problems
• Apply modern engineering techniques to design, develop, deploy, and maintain end-to-end AI workflows
• Adapt and integrate diverse AI model architectures, including computer vision, NLP, audio processors, LLMs, and multi-modal frameworks
• Monitor and maintain AI products through systematic identification of performance degradation and computational inefficiency
• Maintain knowledge of current AI research and adapt emerging techniques to intelligence applications
• Test and evaluate AI solutions against mission requirements and produce actionable recommendations

Requirements

• Ability to pass the Data Science Examination (DSE)
• Transcripts for each academic institution (unofficial accepted initially)
• Degree in Computer Science, Engineering, Information Systems, Information Technology, Mathematics, Applied Mathematics, Statistics, Applied Statistics, Operations Research, Artificial Intelligence, Data Science, or Physical or Biological Sciences

Qualifications

• Associate's, Bachelor's, Master's, or Doctoral degree in Computer Science, Engineering, Information Systems, Information Technology, Mathematics, Applied Mathematics, Statistics, Applied Statistics, Operations Research, Artificial Intelligence, Data Science, or Physical or Biological Sciences.
• Entry level: Associate's degree plus 2 years of relevant experience (Computer Science/Engineering) or 3 years (other fields), or Bachelor's degree with no experience (CS/Engineering) or 1 year (other fields), or Master's degree with no experience. Relevant experience includes implementing production scale AI/ML solutions, distributed model training, distributed AI/ML systems, AI/ML performance monitoring, platform engineering, cloud engineering, developing deep learning models, neural networks, sustaining/maintaining AI/ML models, implementing AI/ML algorithms, AI/ML model development and deployment, DevOps, MLOps, cloud infrastructure management, software engineering, automated testing, or containerization.

Skills

* Required skills

Benefits