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Vetted Amazon S3 Professionals

Pre-screened and vetted.

DZ

Senior Full-Stack .NET Engineer specializing in cloud-native web applications

Texas, United States12y exp
AmazonUC Santa Cruz
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ZM

Mid-level AI/ML Engineer specializing in generative AI, LLMs, and MLOps

Los Angeles, CA6y exp
NVIDIACalifornia State University, Dominguez Hills
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AM

Mid-level Software Engineer specializing in full-stack and distributed backend systems

San Francisco, CA5y exp
StripeSaint Louis University
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JH

Senior Full-Stack Software Engineer specializing in web platforms and AI-enabled experiences

Springville, CA8y exp
MetaWest Virginia University
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HV

Senior Data Engineer specializing in cloud-native data platforms and streaming pipelines

7y exp
GoogleUniversity of Cincinnati
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SS

Mid-level Applied AI Engineer specializing in LLMs, MLOps, and real-time AI systems

CA, USA3y exp
Google DeepMindUniversity of North Texas
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SO

Mid-level AI/ML Engineer specializing in LLMs, multilingual NLP, and low-latency MLOps

CA, USA6y exp
MetaClarkson University
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QH

Senior AI/ML & Data Scientist specializing in NLP, knowledge graphs, and semantic search

Mountain View, CA10y exp
IntuitUniversity of Illinois Chicago
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WY

Senior Software Engineer specializing in Applied AI and scalable backend systems

6y exp
RampDuke University
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AC

Executive FinTech Founder and Software/Finance Leader specializing in data pipelines and valuation

Chicago, IL34y exp
QBmetricsStanford University
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NV

Senior AI/ML Engineer specializing in LLM agents, RAG, and production ML systems

San Francisco, CA7y exp
OpenAISaint Louis University
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AG

Senior Software Engineer specializing in cloud security and identity management

Chicago, IL8y exp
AmazonUniversity of Illinois Urbana-Champaign
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AD

Senior Full-Stack & AI/ML Engineer specializing in cloud-native SaaS and IoT analytics

Corpus Christi, TX11y exp
MN InfotechNYU
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VV

Executive IT & Cloud Architect specializing in AWS, Salesforce, and AI/ML

25y exp
Connected World TechMIT Sloan School of Management
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BP

Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps

Austin, TX5y exp
MetaTexas A&M University-Kingsville
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DA

Mid-level Machine Learning Engineer specializing in Generative AI and LLM applications

USA6y exp
OpenAINJIT
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JW

Senior Product & Application Support Leader specializing in enterprise SaaS and cloud platforms

San Francisco Bay Area13y exp
AmazonPurdue University
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BT

Senior Cloud Engineer specializing in AWS/Azure infrastructure, DevOps, and cloud-native platforms

Remote10y exp
SnowflakeUniversity of Texas at Austin
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MC

Senior Software Engineer specializing in AI for Healthcare and Enterprise SaaS

Seattle, WA9y exp
Amazon
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AS

Junior Data Scientist specializing in LLM agents, RAG, and reinforcement learning

Pittsburgh, PA1y exp
McKinsey & CompanyCarnegie Mellon University

McKinsey practitioner who built and deployed production LLM systems for consultants/clients, including a Power BI-integrated multi-agent chatbot (RAG + text-to-SQL + formatting) with custom Python orchestration, verification loops, and a 100+ case eval set achieving ~95% consistency. Also delivered a taxonomy-mapper agent that standardized inconsistent labeling for C-suite stakeholders, cutting a process from >2 weeks to <30 minutes through demos and business-focused communication.

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YL

Yutao Liu

Screened

Senior Software Engineer specializing in backend services and full-stack web platforms

Palo Alto, CA8y exp
AmazonUC Irvine

Project lead who partners with PM and customers to gather requirements, adjust project plans, and deliver new functionality that drives customer satisfaction and revenue. Has experience building features end-to-end and presenting successful technical demos to engineering and management audiences; no stated experience with LLM/agentic systems.

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SC

Mid-level AI/ML Engineer specializing in Generative AI, LLM alignment, and RAG

CA6y exp
Scale AIUniversity of Texas at Arlington

Built and productionized a real-time enterprise RAG pipeline to improve factual accuracy and reduce LLM hallucinations by grounding responses in constantly changing internal knowledge bases (policies, manuals, FAQs). Experienced in orchestrating end-to-end ML workflows (Airflow/Kubernetes), handling messy multi-format data with schema enforcement (Pydantic/Hydra), and maintaining freshness via streaming incremental embeddings plus batch refresh. Also delivers applied ML solutions with non-technical teams (marketing/CRM) for segmentation and personalized engagement.

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