> Markdown version of [/jobs/ext/3700958-model-development-software-engineer](https://www.wearedevelopers.com/jobs/ext/3700958-model-development-software-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Model Development Software Engineer

- **Company:** Gridiron IT Solutions LLC
- **Location:** Herndon, VA, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** AI Evaluation, Artificial Intelligence, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Software Applications, Cloud Computing, Cloud Engineering, Identity and Access Management, Python (Programming Language), Machine Learning, Secure Coding, Cloud Platform System, Large Language Models, Prompt Engineering, Generative AI, AWS Lambda, Amazon Virtual Private Cloud (VPC), Amazon Relational Database Service, Information Technology, Data Analytics, Machine Learning Operations, Serverless Computing
- **Published:** October 12, 2026
- **Apply:** https://www.clearancejobs.com/jobs/9235759/model-development-software-engineer

## About the Role

* Active TS/SCI clearance with CI Polygraph.
 * Minimum 3 years of experience with AI model development tools, techniques, and performance evaluation methodologies.
 * Minimum 2 years of hands-on Generative AI model development experience.
 * Experience designing benchmarks and evaluation frameworks for GenAI model performance.
 * Strong background in quantitative analysis and data-driven decision-making.
 * Experience working on cross-functional and multidisciplinary technical teams.
 * Ability to operate effectively within classified and secure development environments., * Experience with AWS services including:
o Amazon EC2
o Amazon S3
o AWS VPC
o AWS IAM
o Amazon RDS
o Amazon SageMaker
o AWS Lambda and other serverless technologies

 * Experience optimizing cloud-based solutions for performance and cost efficiency.
 * Minimum 2 years of cloud architecture and implementation experience.
 * Master's degree or higher in:
o Computer Science
o Data Science
o Artificial Intelligence
o Engineering
o Mathematics
o Related STEM field

 * Proven ability to explain complex technical concepts and data insights to diverse stakeholders., * Large Language Models (LLMs)
 * Model Fine-Tuning and Optimization
 * Retrieval-Augmented Generation (RAG)
 * Prompt Engineering
 * MLOps and Model Deployment
 * AI Evaluation Frameworks
 * Python Development
 * AWS Cloud Architecture
 * SageMaker
 * Machine Learning Operations
 * Data Analytics and Visualization

## Description

We are seeking a highly skilled Model Development Software Engineer to support a National Security customer within AWS Professional Services. This role focuses on developing, deploying, and optimizing advanced AI/ML and Generative AI solutions in classified cloud environments. The ideal candidate will have hands-on experience building and evaluating AI models, deploying scalable cloud-based solutions, and collaborating directly with mission stakeholders to solve complex operational challenges., * Design, develop, and implement secure, scalable AI/ML model development solutions within AWS environments.

 * Select, customize, and fine-tune machine learning and Generative AI models for mission-specific use cases.
 * Develop, test, and deploy AI-powered applications that address real-world operational requirements.
 * Create and execute benchmark frameworks to assess GenAI model accuracy, performance, and effectiveness.
 * Conduct experiments and performance evaluations to optimize models for scalability, efficiency, and mission impact.
 * Collaborate with customer stakeholders to identify high-value AI use cases and translate requirements into technical solutions.
 * Apply quantitative analysis and data-driven methodologies to solve complex business and operational problems.
 * Work across multidisciplinary teams to deliver innovative AI capabilities in secure environments.
 * Communicate technical findings and recommendations to both technical and non-technical audiences.