Vetted Large Language Models (LLMs) Professionals

Ben Ayed

Screened

Executive product leader specializing in AI, security, and connected devices

Fremont, CA22y exp

“Product leader with hands-on experience building and scaling a production Voice AI Assistant for automotive dealerships, combining LLMs, speech technologies, workflow orchestration, and system integrations. Stands out for balancing AI capability with trust, UX, and operational ROI—shipping a system that answered over 90% of incoming calls while improving conversion, reducing workload, and augmenting rather than replacing human staff.”

“Former psychiatrist and educator turned product leader with a strong human-centered AI philosophy. Has led AI-native SaaS and mobile products, managed large cross-functional orgs, and brings distinctive edtech experience including reducing tutor rematch rates by 52% while protecting the tutor-student relationship.”

“Revamped a university academic calendar system into a Python-based calendar generation service, turning a weeks-long manual scheduling workflow into software that generates dozens of valid calendar combinations in under a minute. Also contributed to an Amazon search ML classifier by introducing precision/recall evaluation to better surface critical failure modes and improve prediction quality.”

Prashant Singh

Screened

Mid-level Backend/Platform Engineer specializing in data pipelines, reliability, and AI-assisted ingestion

8y exp

“Backend engineer who built and scaled a blockchain-based e-voting platform at early-stage startup Elemential Labs, balancing decentralization with real-world operability by centralizing control-plane components while keeping the ledger immutable. Has hands-on experience migrating high-throughput ingestion from Kafka to AWS Kinesis with parallel cutover, strengthening data integrity and read-after-write consistency (Elasticsearch), and hardening pipelines against silent data-quality failures via anomaly detection and self-healing automation.”

“Built and owned end-to-end production Conversational AI and RAG systems at Nike for customer support and product search, with strong evidence of measurable impact. Combines LLM product thinking with solid platform engineering, including evaluation pipelines, monitoring, safety controls, and reusable FastAPI frameworks that improved both user outcomes and team velocity.”