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Vetted Retrieval-Augmented Generation (RAG) Professionals
Pre-screened and vetted.
Director-level AI/ML Product Leader specializing in computer vision, robotics, and AR/VR
Staff Full-Stack Software Engineer specializing in cloud platforms and real-time health data
Mid-level AI/ML Engineer specializing in LLM training, RAG, and scalable inference
Staff Machine Learning Engineer specializing in search, ranking, and LLM systems
Director-level Product Leader specializing in AI, FinTech, SaaS, and platform growth
Senior Applied AI Platform Engineer specializing in RAG and LLM systems
Staff Software Engineer specializing in cloud infrastructure and AI platforms
Richard Jackson
Senior Machine Learning Engineer & Solution Architect specializing in cloud AI systems
“Backend/ML platform engineer with Google experience leading Python microservices for an AI-driven recommendation/retrieval system, including PyTorch inference and a retrieval-augmented generation workflow. Strong in production Kubernetes + GitOps (ArgoCD), real-time Kafka/Spark pipelines, and phased on-prem/legacy to AWS/GCP cloud migrations with reliability-focused rollout and rollback practices.”
Staff Software Engineer specializing in FinTech, payments, and AI platforms
Senior AI/ML Engineer specializing in LLMs, recommendation systems, and ML platforms
Senior AI/ML Engineer specializing in LLM applications, RAG systems, and MLOps
Staff Machine Learning Scientist specializing in NLP, LLMs, and Generative AI
Senior Software Engineer specializing in backend, AI workflows, and distributed systems
Senior Full-Stack Engineer specializing in AI platforms and compliance systems
Senior Software Engineer specializing in ML, distributed systems, and GenAI search
Senior Full-Stack Software Engineer specializing in AI-powered distributed systems
Senior Full-Stack Engineer specializing in AI and GenAI platforms
Chase Bradley
Staff AI Engineer specializing in LLM systems, retrieval, and ML infrastructure
“ML/LLM engineer from Cohere who has owned retrieval, reranking, agentic workflows, and internal evaluation infrastructure end-to-end in production. Particularly strong in turning brittle RAG and research-heavy ideas into scalable enterprise systems with grounded outputs, lower customer escalations, and adoption by major clients like Notion and Fujitsu.”