Amazon
Delivery Consultant- AI/ML, Data & Machine Learning (DML)
$131K–$178K
Arlington, VA +1 · Full-time · Posted Jul 11, 2026
- Location
- Arlington, VA
Herndon, VA - Experience
- 3+ years in machine learning/statistical modeling and data analysis; 2+ years in computer vision. Preferred: 2+ years of cloud architecture and solution implementation.
- Education
- Preferred: Master's degree or above in Science, Technology, Engineering, or Mathematics (STEM).
- Visa sponsorship
- Candidate must be a US Citizen and currently possess and maintain an active TS/SCI security clearance.
About the job
Designs and delivers scalable, secure AI/ML and generative AI solutions for AWS customers, from identifying use cases through deployment and optimization.
What you'll do
- Design, implement, and scale customer-specific AI/ML solutions on AWS, including selecting and fine-tuning models.
- Develop and deploy machine learning models and generative AI applications; run experiments and optimize performance.
- Advise customers on use cases, implementation strategies, responsible AI practices, and emerging technologies.
What they ask for
- Knowledge of professional software engineering practices across the full software development life cycle.
- 3+ years of experience with machine learning/statistical modeling, data analysis tools and techniques, and performance parameters.
- Experience defining and creating benchmarks for assessing GenAI model performance and applying quantitative analysis to business problems in cross-disciplinary projects.
- 2+ years of computer vision experience.
Nice to have
- Knowledge of AWS services across compute, storage, networking, security, databases, machine learning, and serverless technologies.
- Master's degree or above in STEM; 2+ years of cloud architecture and solution implementation experience.
Pay
USD 131,300.00 - 177,600.00 annually (base salary); USD 131,300.00 - 177,600.00 annually (base salary)
Tools
AWS, GenAI
AI focus
Builds and deploys machine learning and generative AI solutions, including model selection and fine-tuning, performance benchmarking, experimentation, and responsible application.
Model-written summary · the original posting is authoritative