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Vetted Amazon Bedrock Professionals
Pre-screened and vetted.
Junior Robotics & ML Engineer specializing in vision-language-action autonomy
Mid-level Generative AI/ML Engineer specializing in LLMs, RAG, and MLOps
Senior AI/ML Engineer specializing in Generative AI, RAG, and multimodal LLM systems
Mid-level Machine Learning Engineer specializing in Generative AI and LLMOps
Mid-level Software Engineer specializing in FinTech and AI-powered systems
Senior Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Junior AI/ML Engineer specializing in Generative AI production systems
Junior Full-Stack AI Developer specializing in multi-agent LLM systems on AWS
Junior AI/ML Engineer specializing in LLM applications, RAG, and multimodal computer vision
Mid-Level Software Engineer specializing in ML and Generative AI applications
Mid-Level ML/AI Engineer specializing in LLMs, RAG, and multi-agent systems
Mid-level AI/ML Engineer specializing in risk modeling, healthcare analytics, and MLOps
Mid-Level Software Engineer specializing in cloud microservices and AI automation
Senior Cloud & DevOps Engineer specializing in AWS migration, IaC, and CI/CD
Mid-level Front-end Engineer specializing in SaaS, AI, and Healthcare IT
Executive Fractional CTO specializing in multi-cloud, data platforms, and AI/ML architectures
Tony Barreto
Mid-Level Full-Stack Software Developer specializing in modern web apps
“Product-focused full-stack builder who has shipped and operated multiple production apps from scratch, including an e-commerce bakery delivery scheduler (with concurrency controls and timezone handling) and a real-time passenger music-request system for Lyft rides that hit and resolved YouTube API rate-limit scaling issues via debouncing and caching. Strong in React+TypeScript and Node.js/TypeScript backends, with solid PostgreSQL/PostGIS data modeling and performance tuning.”
Rukmini Pisipati
Junior AI/ML Engineer specializing in LLM automation and NLP
“Built and shipped a production LLM hallucination detection and monitoring pipeline using semantic-level entropy (embedding-clustered multi-generation variance) to flag unreliable outputs in downstream automation. Implemented a scalable async architecture (FastAPI + Docker + Redis/Celery) with strong observability (structured logs + PostgreSQL) and developed evaluation loops combining controlled prompts and human review; also partnered with non-technical stakeholders on AI-driven form validation/document processing.”