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Vetted AI & Machine Learning Professionals
Pre-screened and vetted.
Popular Titles
Senior Machine Learning Engineer specializing in MLOps and LLM/Agentic AI systems
Junior GenAI/ML Engineer specializing in LLM agents and production NLP systems
Junior Robotics & AI/ML Engineer specializing in autonomous systems and computer vision
Intern Machine Learning Engineer specializing in Generative AI and LLM systems
Mid-level AI & Machine Learning Engineer specializing in production ML and LLM applications
Mid-level Machine Learning & Data Engineer specializing in MLOps and cloud data platforms
Mid-level Machine Learning Engineer specializing in optimization, RL, and graph neural networks
Mid-level AI & Machine Learning Engineer specializing in computer vision and MLOps
Mid-level AI/ML Engineer specializing in production ML, NLP, and computer vision
Mid-level Machine Learning Engineer specializing in GenAI, LLM agents, and MLOps
Mid-level Machine Learning Research Engineer specializing in foundation models and GenAI
Intern Machine Learning Engineer specializing in NLP and LLM/RAG systems
Mid-level AI/ML Engineer specializing in NLP, Computer Vision, and Generative AI
Staff Machine Learning Engineer specializing in Generative AI, MLOps, and Computer Vision
Principal AI Platform Architect specializing in agentic AI and enterprise LLM infrastructure
Mid-level AI/ML Engineer specializing in GenAI, MLOps, and big data on cloud platforms
Intern AI/ML Engineer specializing in generative AI and multimodal agentic systems
Senior AI/ML Engineer specializing in production AI systems for healthcare and finance
Mid-level Generative AI Engineer specializing in LLMs, NLP, and multimodal systems
Vismay Devjee
Mid-level GenAI Engineer specializing in AI agents, RAG, and LLM evaluation
“Asset Management Risk professional at Fidelity Investments who built and productionized an agentic RAG platform enabling compliance and analysts to query 10,000+ fund documents with cited answers in seconds. Implemented structure-aware semantic chunking (AWS Textract), hierarchical retrieval, and hybrid search to raise accuracy from 68% to 94%, and built an evaluation framework tracking accuracy/latency/cost/hallucinations—delivering 40+ hours/month saved and zero critical production failures.”