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Vetted Model Evaluation Professionals
Pre-screened and vetted.
Senior NLP Research Scientist specializing in summarization, argument mining, and LLM evaluation
Mid-level Software Engineer specializing in cloud infrastructure automation and ML systems
Senior Machine Learning Engineer specializing in LLM systems and generative AI
Senior AI/ML Engineer specializing in conversational AI and ML platforms
Mid-level AI/ML Engineer specializing in Generative AI and enterprise machine learning
Mid-level AI/ML Engineer specializing in generative AI and data engineering
Mid-level AI/ML Engineer specializing in GenAI, MLOps, and big data on cloud platforms
Senior Data Engineer specializing in cloud-scale data pipelines and legal data systems
Senior Infrastructure Platform Architect specializing in hybrid cloud and Kubernetes automation
Senior Software Engineer specializing in AI-powered search and backend systems
Senior AI/ML Engineer specializing in conversational AI and contact center automation
Senior AI/ML Engineer specializing in production AI systems for healthcare and finance
Senior Business Analyst specializing in data analytics and business intelligence
Vismay Devjee
Mid-level GenAI Engineer specializing in AI agents, RAG, and LLM evaluation
“Asset Management Risk professional at Fidelity Investments who built and productionized an agentic RAG platform enabling compliance and analysts to query 10,000+ fund documents with cited answers in seconds. Implemented structure-aware semantic chunking (AWS Textract), hierarchical retrieval, and hybrid search to raise accuracy from 68% to 94%, and built an evaluation framework tracking accuracy/latency/cost/hallucinations—delivering 40+ hours/month saved and zero critical production failures.”
Devisri Veeramachaneni
Senior Software Engineer specializing in cloud backend systems and LLM-powered agents
“Amazon Fire TV Devices engineer who built and shipped a production LLM-powered lab triage and validation system that grounds recommendations in internal runbooks/known-issue data and pushes evidence-based actions via dashboards and Slack. Emphasizes safety and measurability with structured JSON outputs, replay-based evaluation on historical incidents, and production metrics (e.g., disagreement rate and time-to-first-action), plus cost/latency optimizations like caching, batching, and rule-based fast paths.”
Nandini Kosgi
Mid-level AI/ML Engineer specializing in LLMs, RAG, and fraud/risk analytics in Financial Services
“Built and shipped a production-grade GenAI Fraud & Compliance Investigation Copilot for a large US bank, integrating OCR docs, structured data, and prior case history to generate grounded, regulator-friendly summaries and red-flag highlights. Demonstrates strong end-to-end LLM systems engineering (LangGraph/LangChain, hybrid retrieval with FAISS+BM25, guardrails/citations, streaming/latency optimization) plus rigorous evaluation and close partnership with compliance stakeholders.”