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Vetted Large Language Models Professionals
Pre-screened and vetted.
Junior AI/ML Engineer specializing in LLMs, recommender systems, and computer vision
Mid-level MLOps/Machine Learning Engineer specializing in cloud-native production ML
Junior Machine Learning Engineer specializing in Generative AI and MLOps
Mid-level Data Scientist / GenAI Engineer specializing in LLM agents, RAG, and OCR
Junior Machine Learning Engineer specializing in scalable ML systems and LLMs
Mid-level Full-Stack Developer specializing in scalable web apps and cloud-native APIs
Senior Software Engineer specializing in AI/ML and cloud backend systems
Intern AI/ML Engineer specializing in NLP, graph analytics, and agentic RAG systems
Junior AI/ML Engineer specializing in LLM applications, RAG, and multimodal computer vision
Mid-level Software Engineer specializing in backend systems and AI applications
Senior Data Engineer specializing in Machine Learning and Healthcare Data Platforms
Junior Software Engineer specializing in full-stack, cloud, and AI/ML integration
Senior Full-Stack Python Engineer specializing in AI/LLM-powered web applications
Rukmini Pisipati
Junior AI/ML Engineer specializing in LLM automation and NLP
“Built and shipped a production LLM hallucination detection and monitoring pipeline using semantic-level entropy (embedding-clustered multi-generation variance) to flag unreliable outputs in downstream automation. Implemented a scalable async architecture (FastAPI + Docker + Redis/Celery) with strong observability (structured logs + PostgreSQL) and developed evaluation loops combining controlled prompts and human review; also partnered with non-technical stakeholders on AI-driven form validation/document processing.”
CharanTeja Kurakula
Entry-Level AI Engineer specializing in NLP and LLM-powered applications
“AI engineer who built an agentic, production-deployed LLM workflow for tobacco violation parsing and automated multi-case creation, using six specialized agents and a human-in-the-loop confidence-threshold routing design. Addressed data privacy constraints by generating synthetic datasets with LLM prompting, and orchestrated reproducible end-to-end pipelines in LangChain with robust testing and evaluation (precision/recall, micro-F1).”
Shreyas Krishnareddy
Junior AI/Software Engineer specializing in NLP, RAG, and resume parsing
“Backend/AI engineer who built and refactored a production RAG system over IRS Form 990 filings for 60 nonprofits, using a dual-path architecture (deterministic financial ranking + TF-IDF semantic retrieval) to keep latency sub-2s and reduce hallucinations. Demonstrates strong API craftsmanship in FastAPI (contract-first, OpenAPI-driven) plus production-grade security for multi-tenant systems (JWT, RBAC, Supabase-style RLS) and careful migration practices (feature flags, traffic mirroring, incremental rollout).”