AI/ML Engineer
On-siteHerndon, Virginia, United States
Job Summary
Design and maintain scalable cloud-native infrastructure and deployment pipelines using Infrastructure as Code across multiple secure environments. Lead architecture for resilient systems, build optimized CI/CD pipelines for microservices, and develop full-stack applications while implementing monitoring, logging, and security-first solutions. Mentor team members on DevOps best practices and participate in on-call production support rotations. This role requires an active TS/SCI Clearance with Polygraph and onsite work at AWS Herndon or the Springfield customer site. Candidates must have three or more years of experience with machine learning, statistical modeling, and generative AI frameworks.
Required Qualifications
- Active TS/SCI Clearance with Polygraph
- IC Green Badge
- Bachelor's degree or higher in Science, Technology, Engineering, or Mathematics (STEM)
- Experience in professional software engineering and the full Software Development Life Cycle (SDLC), including coding standards, software architecture, code reviews, source control management, continuous deployments, testing, and operational excellence
- Three (3) or more years of experience with machine learning, statistical modeling, data analysis tools, and techniques
- Willingness to work onsite in a SCIF environment at AWS Herndon or the Springfield customer site, up to 100% of the work week
- Willingness to participate in an on-call production support rotation
Desired Qualifications
- Master's degree or higher in Science, Technology, Engineering, or Mathematics (STEM)
- Experience working on multi-team, cross-disciplinary projects
- Experience applying quantitative analysis to solve business problems and support data-driven decision making
- Experience defining and creating benchmarks for evaluating Generative AI model performance
- Experience developing AI/ML solutions using Python, SQL/NoSQL, and APIs
- Experience working with Large Language Models (LLMs), prompt engineering, and generative AI frameworks
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