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Vetted Generative AI Engineers
Pre-screened and vetted.
Mid-level Generative AI Engineer specializing in LLM agents, RAG systems, and workflow automation
Mid-level Full-Stack GenAI/ML Engineer specializing in RAG and agentic AI on AWS/Azure
Mid-level Generative AI Engineer specializing in LLMs, RAG, and semantic search
Junior Software Engineer specializing in Generative AI and full-stack development
Senior Machine Learning Engineer specializing in Generative AI and LLM applications
Mid-level Generative AI Developer specializing in LLM agents and RAG systems
Mid-level Generative AI Engineer specializing in RAG, LLMOps, and enterprise decision intelligence
Mid-level Data & AI Engineer specializing in cloud data pipelines and LLM-powered analytics
Mid-level AI/ML Engineer specializing in Generative AI, RAG, and LLM agent workflows
Mid-level AI/ML Engineer specializing in Generative AI, RAG, and multi-agent LLM systems
Mid-level Data Scientist / ML Engineer specializing in Generative AI and LLM/RAG systems
Mid-Level Full-Stack Developer specializing in cloud-native microservices and GenAI
Mid-Level Generative AI Engineer specializing in LLM apps, RAG, and cloud deployment
vedavathi thumula
Mid-level GenAI/ML Engineer specializing in agentic AI and RAG systems
“Backend/platform engineer who has owned a Python/FastAPI results API and deployed it on Kubernetes with Helm and GitHub Actions-driven CI/CD. Demonstrates strong production operations mindset across performance tuning, monitoring, safe rollouts/rollbacks, and phased migrations, plus hands-on Kafka streaming experience focused on ordering and idempotency.”
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.”