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Vetted GPT-4 Professionals
Pre-screened and vetted.
Mid-level Data Scientist specializing in GenAI, NLP, and recommendation systems
Mid-Level Full-Stack Software Engineer specializing in cloud-native and AI-driven applications
Senior Data Scientist specializing in ML, fraud risk, and Generative AI (RAG/LLMs)
Mid-level GenAI/ML Engineer specializing in LLM agents, RAG, and document intelligence
Mid-level AI/ML Engineer specializing in Generative AI agents and enterprise analytics
Mid-level Software Engineer specializing in GenAI and distributed systems
Senior Full-Stack .NET Engineer specializing in cloud, APIs, and data platforms
Senior Software Engineer specializing in full-stack distributed systems and AI
Mid-level AI/ML Engineer specializing in conversational AI, NLP, and LLM-powered RAG systems
Mid-level AI/ML Software Engineer specializing in Generative AI and NLP
Jayeetra Bhattacharjee
Mid-level AI/ML Engineer specializing in LLMs, NLP, and analytics automation
“AI/ML Engineer (TCS) who built and deployed a production LLM-powered audit transaction validation service to reduce manual review of unstructured transaction records and comments. Implemented a LangChain/Python pipeline for extraction/normalization and discrepancy detection, with strong production reliability practices (decision logging, dashboards, labeled eval sets) and a human-in-the-loop auditor feedback loop to improve precision/recall under strict data-sensitivity and near-real-time constraints.”
Srinivas Tenneti
Mid-level AI/ML Engineer specializing in GenAI and predictive modeling
“Built and deployed a GPT-4-powered medical assistant for clinical staff to reduce time spent searching guidelines and EHR information, with a strong emphasis on safety and compliance. Uses strict RAG, confidence thresholds, and fallback behaviors to prevent hallucinations, and runs production-grade workflows orchestrated with LangChain/LangGraph plus Docker/Kubernetes/MLflow and monitoring for reliability and cost.”
Vamsi Koppala
Mid-level Machine Learning Engineer specializing in Generative AI and RAG systems
“LLM/ML engineer who has shipped an enterprise RAG-based Q&A system (LangChain/LlamaIndex, FAISS + Azure Cognitive Search, GPT-3.5/4 via OpenAI/Azure OpenAI) to production on Docker + Kubernetes/OpenShift, tackling hallucinations, retrieval quality, latency/cost, and RBAC/IAM security. Also partnered with operations leaders to turn manual reporting into an LLM-powered summarization and forecasting dashboard driven by real KPIs and iterative stakeholder feedback.”
AnilKumar Kanakadandila
Mid-level Data & AI Engineer specializing in data engineering, analytics, and LLM/RAG apps
“Built a production RAG-based “unified assistant” that consolidates siloed company documents into a single chatbot while enforcing fine-grained access control via RBAC/metadata filtering with OAuth2/JWT. Experienced orchestrating LLM workflows with LangChain/LangGraph + FastAPI (async + caching) and measuring performance via retrieval accuracy and response-time SLAs. Also delivered a churn analytics solution with dashboards and automated retention campaigns using n8n.”
shiva kumar kotha
Mid-level AI/ML Engineer specializing in Generative AI and healthcare data
“Built and deployed a production RAG-based document Q&A system on Azure OpenAI to help business teams search thousands of PDFs/Word files, using Qdrant vector search, MongoDB, and a Flask API. Demonstrates strong production engineering (streaming large-file ingestion, parallel preprocessing, monitoring/retries) plus systematic prompt/embedding/chunking experimentation to improve accuracy and reduce hallucinations, and has hands-on orchestration experience with ADF/Airflow/Databricks/Synapse.”