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Vetted GPT-4 Professionals
Pre-screened and vetted.
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.”
MARCUS JOHNSON
Senior AI/ML Engineer specializing in Generative AI, NLP, and RAG systems
“ML/NLP engineer focused on production-grade data and search/recommendation systems: built an end-to-end pipeline that connects unstructured customer feedback with product data using TF-IDF/BERT, Spark, and AWS (SageMaker/S3), orchestrated with Airflow and monitored for drift. Also has hands-on experience with entity resolution at scale and improving search relevance via BERT embeddings, FAISS vector search, and domain fine-tuning validated with precision@k and A/B testing.”
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multi-agent systems
Senior Software Engineer specializing in AI/ML evaluation and full-stack systems
Senior Backend Engineer specializing in scalable AWS serverless and data pipelines
Mid-level Generative AI & Machine Learning Engineer specializing in LLMs and RAG
Principal Data Scientist / AI Engineer specializing in healthcare-native AI platforms
Ashi Sinha
Junior Software Engineer specializing in full-stack and ML/NLP systems
“Entry-level full-stack engineer with internship experience at Amazon (Appstore IAP flow + uninstall recommendation workflow) and a health-tech startup (OneVector) where they built a DSUR reporting workflow end-to-end, including document generation, S3-backed versioning/metadata, and secure preview/download. Demonstrates strong production debugging and reliability mindset (instrumentation, deterministic retrieval, idempotent writes) and focuses on UX/performance in high-stakes user flows.”
Angel Contreras
Senior Data Scientist specializing in machine learning, NLP, and MLOps
“ML/NLP engineer with experience building production-grade legal-tech and data platforms, including a GPT-4/LangChain contract review system using ElasticSearch embeddings (RAG) deployed on AWS EKS. Strong in entity resolution and scalable batch/streaming pipelines (Kafka/Spark), with measurable impact (70%+ reduction in contract review time) and a focus on monitoring and CI/CD for reliable delivery.”
Senior Software Engineer specializing in distributed systems, AI/ML platforms, and cloud-native SaaS
Mid-level AI Engineer specializing in LLM applications and personalization
Staff AI/ML Engineer specializing in NLP, recommender systems, and Generative AI
Mid-level Machine Learning Engineer specializing in MLOps, RAG, and real-time personalization
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and real-time recommendation systems
Senior Software Engineer specializing in scalable cloud microservices and AI integration
Staff Machine Learning Engineer specializing in LLMs, recommendations, and MLOps
Principal AI Architect specializing in GenAI, agentic systems, and RAG
Mid-level Machine Learning Engineer specializing in GenAI, forecasting, and MLOps
Mid-level Machine Learning Engineer specializing in fraud detection and recommendations
Yue Yang
Intern Data Scientist specializing in GenAI (LLMs, RAG) and ML model optimization
“Built and deployed a production LLM-powered risk assistant for KPMG and Freddie Mac that lets analysts query a confidential Neo4j risk graph in natural language (no Cypher), turning multi-day analysis into minutes with traceable, cited answers. Implemented rigorous guardrails, deterministic verification, RBAC/security controls, and a full eval/observability stack, cutting query error rate by ~50% and iterating through weekly UAT with non-technical risk analysts.”