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Vetted AI Agents Professionals
Pre-screened and vetted.
Director-level AI & Technology Executive specializing in enterprise AI platforms
Senior Full-Stack Java Developer specializing in microservices, cloud, and AI agent systems
Mid-level Software Engineer specializing in AI automation and full-stack systems
Senior Cloud DevOps Engineer specializing in multi-cloud (AWS/Azure) architecture and automation
Intern Data Scientist specializing in LLMs, RAG, and computer vision
Mid-level AI Engineer specializing in LLM agents, RAG, and production automation
Executive AI & Data Platform Leader (CAIO/CTO) specializing in enterprise-scale cloud and ML
Mid-level Generative AI Engineer specializing in LLM agents and RAG for enterprise workflows
Senior Data Scientist specializing in AI agents, fraud detection, and cloud ML platforms
Intern software engineer specializing in AI and full-stack systems
Mid-Level Software Engineer specializing in cloud microservices and GenAI RAG systems
Mid-Level Backend Java Engineer specializing in microservices and cloud-native systems
Mid-level Full-Stack Developer specializing in Python, microservices, and AI platforms
Senior AI Architect specializing in Generative AI and LLM systems
Mid-level DevOps & Platform Engineer specializing in Kubernetes and AWS infrastructure
Senior Customer Success Manager specializing in Technical B2B SaaS
Sanjay Mamani
Executive Technology Leader specializing in enterprise data, AI, and cloud analytics
“25-year builder/operator who has scaled others' visions and led VC-backed startup incubation work (Saltmines). Built Bridgetree’s AI CoE from 0 to 1 and cites $20M+ measurable customer impact, with experience leading 110-person cross-disciplinary teams. Exploring a new venture idea (gotAgentic.ai) focused on agentic AI solutions such as AI-ready data prep, agentic SDLC teams, and front-office automation (scheduling/invoicing).”
Bhavana G
Mid-level GenAI Engineer specializing in AI agents and RAG systems
“Built and deployed a production LLM-based RAG agent platform adopted by multiple business teams (Marketing, GTM, Recruiting, Customer Support) to automate knowledge search, Q&A, and content generation. Emphasizes production-grade reliability (grounding/validation/guardrails), rigorous evaluation/monitoring, and cost-aware scaling via model tiering, prompt/retrieval optimization, and caching using LangChain/LangGraph orchestration.”