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Vetted XGBoost Professionals
Pre-screened and vetted.
Yuxin Xiong
Intern Machine Learning Engineer specializing in LLM reasoning, agents, and deployment
“AWS AI Lab engineer who deployed a production Chain-of-Thought analytical agent for tabular reasoning, emphasizing grounded tool-constrained workflows with schema-validated intermediate outputs. Built robust evaluation/logging with step-level observability to catch regressions across model versions, and has experience scaling distributed LLM training via Slurm + DeepSpeed/FSDP with checkpointing and failure recovery.”
Dexin Huang
Junior AI Engineer specializing in LLM systems, RAG, and full-stack automation
“Built and deployed an AI receptionist product for field-service businesses (HVAC/electrician), including real-time Jobber scheduling integrations and Twilio-based calling. Combines hands-on customer/operator shadowing with strong production engineering (queueing to handle API limits, rigorous testing/mocking, mirrored prod environment) and cross-layer troubleshooting, driving user adoption through review/override workflows.”
Gabrielle Burns
Senior AI/ML Engineer specializing in computer vision, NLP, and enterprise ML systems
“ML/AI engineer with hands-on ownership of production computer vision and GenAI systems, spanning real-time public safety video analytics and RAG-based knowledge assistants. Stands out for translating research-oriented approaches into scalable, monitored production systems with clear business impact, including 50% latency reductions, 25% faster response times, and 40% lower document search time.”
jiawei Li
Intern Applied Scientist specializing in LLM agents for software engineering
“Applied Scientist intern at Amazon who built a production-adopted LLM-judge to evaluate an agentic chatbot’s intermediate reasoning and tool calls using a knowledge-graph grounding approach. Also published award-winning work (ACM SIGSOFT Distinguished Paper) using LangChain + GPT-4 tools to generate factually grounded commit messages, with rigorous human-centered evaluation metrics.”
Krishna Sahith Poruri
Mid-level AI/ML Engineer specializing in LLMs, RAG, and MLOps
“ML/LLM engineer who built a production RAG system (GPT-4 + FAISS + FastAPI) to deliver fast, grounded answers from proprietary documents, optimizing for sub-200ms latency and high-concurrency scale. Strong MLOps/observability background: drift monitoring with Prometheus + Streamlit, automated retraining via Airflow, Kubernetes autoscaling, and MLflow-managed model lifecycle, plus inference cost reduction through quantization and structured pruning.”
Mid-level AI/ML Engineer specializing in NLP, computer vision, and MLOps
Mid-level AI/ML Engineer specializing in recommender systems, fraud detection, and LLMs
Mid-level AI/ML Engineer specializing in NLP/LLMs and production ML systems
Mid-level Machine Learning Engineer specializing in LLMs and RAG systems
Intern AI/ML Engineer specializing in LLM agents, RAG, and computer vision
Mid-level Machine Learning Engineer specializing in NLP, recommender systems, and on-device ML
Mid-Level Full-Stack Software Engineer specializing in FinTech and cloud-native AI systems
Mid-level AI/ML Engineer specializing in GPU-accelerated LLM and vision systems
Senior AI/ML Engineer specializing in personalization, recommendations, and forecasting
Mid-level Business Analyst specializing in data analytics and financial systems
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and GPU-accelerated cloud systems
Mid-level Generative AI & Machine Learning Engineer specializing in LLMs and RAG