Amazon
Sr. Delivery Consultant - AI/ML, AWS Professional Services
$154K–$239K
Jersey City, NJ +9 · Senior · Full-time · Posted May 29, 2026
- Location
- Jersey City, NJ
Seattle, WA
Arlington, VA
Atlanta, GA
New York, NY
Denver, CO
Herndon, VA
Chicago, IL
Mountain View, CA
Dallas, TX - Experience
- 5+ years building machine learning models; 3+ years in production AI/ML model work; 3+ years customer-facing; 3+ years coding, querying, and scripting. Preferred: 2+ years designing, deploying, and evaluating AI agents.
About the job
Designs and delivers customer-specific generative AI and machine learning solutions on AWS, advising customers throughout implementation and deployment.
What you'll do
- Design, build, and implement scalable, secure GenAI and ML applications and models on AWS.
- Guide customers on model training, building, evaluation, deployment, and AI/ML service implementation.
- Advise customers on technology trends and share expertise through mentoring, training, and reusable artifacts.
What they ask for
- 5+ years of building machine learning models for business application experience.
- 3+ years of hands-on experience training, fine-tuning, evaluating, and deploying AI/ML models in production.
- 3+ years of customer-facing work solving business problems with advanced technologies.
- Experience with cloud services related to machine learning and generative AI applications; 3+ years of coding, data querying languages, and scripting languages.
Nice to have
- Knowledge of AWS services including SageMaker, Bedrock, EC2, ECS, EKS, OpenSearch, and AWS certifications.
- 2+ years designing, deploying, and evaluating AI agents and orchestration approaches; experience with LangChain, LangGraph, LlamaIndex, or similar tools.
Pay
176,600.00 - 239,000.00 USD annually (base salary); 153,600.00 - 207,800.00 USD annually (base salary); 169,000.00 - 228,600.00 USD annually (base salary)
Tools
AWS, Amazon SageMaker, Bedrock, EC2, ECS, EKS, OpenSearch, SQL
AI focus
Build and deploy generative AI and machine learning applications and models on AWS, including production model training, fine-tuning, evaluation, and deployment.
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