9th Way Insignia
AI Engineer
About this role
9th Way Insignia seeks an AI Engineer, Level 3 to design and deploy secure, enterprise-scale AI and machine learning solutions for the VA Cybersecurity Operations Systems Engineering program. You'll develop production-ready AI/ML models, evaluate generative AI and LLM technologies, and ensure AI systems meet federal government security and reliability standards.
What you'll do
- Design, develop, train, test, deploy, and maintain AI/ML models for enterprise government applications
- Operationalize production AI/ML solutions ensuring reliability, scalability, and VA mission alignment
- Collect, clean, and prepare structured and unstructured data for model development and evaluation
- Evaluate AI technologies, frameworks, and architectures to recommend solutions for federal cybersecurity use cases
- Develop and support generative AI and LLM solutions including fine-tuning, safety filters, and content moderation
- Perform AI red-team testing and evaluation to identify vulnerabilities, bias, and security risks in model outputs
What they're looking for
- AI and machine learning model development and deployment
- Large language models (LLM) and generative AI
- Data collection, cleaning, and feature engineering
- AI/ML model evaluation, validation, and benchmarking
- Python or similar ML programming languages
- Cybersecurity principles and federal compliance frameworks
- AI red-teaming and adversarial testing
- Cloud platforms and MLOps practices
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9th Way Insignia
9th Way Insignia builds secure, modernized software applications and data solutions for government clients, specializing in full-stack development, cloud architecture, and enterprise analytics. The company is hiring software developers, ETL developers, and Power BI developers to design and maintain applications, data pipelines, and reporting systems using Agile practices and modern technologies.
View all jobs at 9th Way InsigniaLikely interview questions
- Describe your experience developing and deploying machine learning models in production environments—what challenges did you encounter and how did you address them?
- How have you approached evaluating and mitigating bias in AI models, particularly in high-stakes applications?