.png){kind=logo}
Vetted spaCy Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in LLMs, RAG pipelines, and MLOps
Mid-level Machine Learning Engineer specializing in real-time fraud detection and edge AI
Mid-level Machine Learning Engineer specializing in MLOps, RAG, and real-time personalization
Junior AI Prompt Engineer specializing in LLMs, RAG, and conversational AI
Intern Computer Vision/ML Engineer specializing in mapping, localization, and scalable inference
Senior Software Engineer specializing in backend, data pipelines, and AI systems
Mid-level AI/ML Engineer specializing in LLM RAG pipelines and cloud MLOps
Mid-level AI/ML Engineer specializing in GenAI agents and production ML systems
Mid-level Machine Learning Engineer specializing in MLOps and Generative AI
Mid-level Data Scientist/ML Engineer specializing in LLMs, NLP, and recommender systems
Senior AI/ML Engineer specializing in healthcare LLMs and conversational AI
Senior Machine Learning Engineer specializing in conversational AI and InsurTech
Mid-level AI/LLM Engineer specializing in generative AI and ML systems
Mid-level Data Scientist specializing in FinTech and product analytics
Kevin Allen
Senior AI/ML Engineer specializing in conversational and generative AI
“Built and productionized an LLM-based support assistant end-to-end, including RAG, APIs, monitoring, guardrails, and agent feedback loops. Stands out for translating GenAI prototypes into reliable production systems with structured evaluation, safety controls, and reusable Python infrastructure that improved both support quality and engineering velocity.”
Suraj Botcha
Intern AI/ML Engineer specializing in LLM systems and industrial AI
“Full-stack AI engineer who has built both document-intelligence products and agentic investigation systems end to end. At ControlRooms.AI, they helped ship a production-facing root cause investigation workflow for industrial operations using Neo4j, FastMCP, RAG, OCR/VLM inputs, and multiple LLMs, contributing to roughly a 10x reduction in manual investigation time. They stand out for designing explainable, traceable AI systems that surface evidence, uncertainty, and missing context rather than forcing overconfident answers.”
Jacqueline Zhang
Mid-level Machine Learning Engineer specializing in LLMs, fairness, and healthcare ML
“ML/NLP practitioner with a master’s thesis focused on domain-adaptive knowledge distillation for LLMs (LLaMA2/sheared LLaMA), showing improved perplexity and ROUGE-L on biomedical data. Also built real-world data linking and search systems: integrated ClinicalTrials.gov with FAERS using fuzzy matching + embeddings, and delivered an LLM-powered FAQ recommender at Hyperledger using sentence-transformers, FAISS, and fine-tuning to mitigate embedding drift.”