Vetted Schema Design Professionals

“Data/ML practitioner with experience spanning medical imaging (retinal vessel analysis for hypertension/CVD risk prediction) and enterprise data engineering at Carl Zeiss. Built large-scale SAP data cleaning/validation pipelines (10M+ daily records, ~99% accuracy) and RAG-based semantic search with LangChain/vector DBs that cut manual querying by 82%, plus automation that reduced data onboarding from 8 hours to 12 minutes.”

“Built and shipped a production LLM-powered “Vet agent” that automates pet symptom intake across multimodal inputs (images/files/text/speech) and provides analysis/home-care guidance, reaching thousands of daily active users within two months. Demonstrates strong agent engineering fundamentals: state-machine orchestration with structured JSON, tool/schema validation, high-availability routing/failover, and rigorous offline/online evaluation loops with trace-driven reliability improvements.”

“Backend engineer who led the end-to-end modernization of FleetView into a scalable, event-driven system supporting 1,000+ users and 13,000+ assets, cutting API latency by ~40%. Also built an AI-powered exit interview analytics pipeline on Azure using GPT-4o with strong guardrails, validation, and evaluation practices, showing a rare mix of production backend rigor and applied LLM workflow experience.”

“Software engineer with experience spanning an SEL internship and Walmart, combining backend/data pipeline work (Python, Kafka, relational DBs) with DevOps practices (Docker, Grafana, GitHub/Jenkins CI/CD, GitOps). Notably contributed to a REST-to-GraphQL migration aimed at reducing cloud utilization and implemented testing strategies to validate the transition.”

“Player-coach engineering leader currently running a Solution Architecture/FDE team responsible for both presales and postsales delivery. Stands out for combining enterprise systems thinking with hands-on AI product work: they built configurable tooling that sped delivery by ~30%, drove a Kafka-to-Pulsar architecture shift for scale, and spent the last two years building LLM-based document extraction and RAG inference pipelines shaped directly by user feedback.”

Javon Lee

Screened

Senior Software Engineer specializing in AI platforms and cloud-native systems

Baltimore, MD8y exp

“Engineer with startup CTO experience and recent hands-on full-stack work at Microsoft and Clarity, focused on compliance and AML workflow platforms for financial services. Stands out for building scalable data and audit systems that reduced manual processing and improved performance, while operating effectively in ambiguous early-stage environments.”

Suman Madipeddi

Screened

Junior AI/ML Engineer specializing in agentic AI, RAG, and voice systems

San Jose, CA2y exp

“Full-stack AI product engineer who has owned production-grade document intelligence and agent systems at meaningful scale, including a copilot used by 10,000+ users and 1M+ queries. Particularly strong in combining React/TypeScript product work with Python/FastAPI, RAG, knowledge graphs, observability, and performance tuning—cutting latency from ~7 seconds to 0.5 milliseconds while improving trust through citations and human review.”

“Backend engineer focused on Python/FastAPI microservices, with hands-on experience deploying to AWS (EKS/ECR) via Jenkins and GitOps-style workflows using ArgoCD. Has built and stabilized real-time Kafka payment-event streaming pipelines and improved production performance under peak load through Redis caching, SQL optimization, and robust retry/DLQ patterns. Also supported phased migrations from on-prem environments to AWS with gradual traffic shifting and monitoring.”

“Built and productionized an NLP-powered customer support assistant at JPMorgan Chase for digital banking, focused on reducing response time for repetitive client queries. Strong in real-world AI deployment challenges—sensitive data handling, low-latency FastAPI services, and AWS/Kubernetes operations with CI/CD—plus a metrics- and guardrails-driven approach to reliable AI workflows.”

Dhyey Desai

Screened

Intern AI/ML Engineer specializing in RAG, multimodal AI, and LLM systems

Los Angeles, California0y exp

“Built and shipped 'PetPulse,' a production AI pet-health note system that records voice notes, transcribes them, converts transcripts into structured symptom/event data, and supports grounded Q&A over a user’s notes and vet PDFs. Demonstrates full-stack LLM product execution (FastAPI + GPT-4 + Firebase), with concrete reliability/performance work (async endpoints, caching, RAG/embeddings, function calling) and user-centered iteration with a non-technical product stakeholder.”

“Full-stack engineer who has built and shipped production Next.js (App Router + TypeScript) applications end-to-end, including an authenticated dashboard with protected routing and post-deploy troubleshooting. Designed Postgres schemas for collaboration/analytics (users/workspaces/sessions/events) and achieved ~60% query-time reduction through indexing and query-plan-driven optimization, with a strong emphasis on accessibility and server-first React architecture.”

“Backend/AI workflow engineer who built production-grade orchestration systems for hardware security verification at Silicon Assurance (Nextflow/Python/Postgres) and a multi-agent LLM-driven regulatory code checking system at the University of Florida. Emphasizes reliability: strict plan/execute/verify boundaries, queue-based isolation, and strong observability/auditability with Prometheus/Grafana and persisted prompts/tool calls.”

“Full-stack product engineer with hands-on ownership across React/TypeScript, serverless backends, and Postgres, combining technical depth with strong UX instincts. Stands out for measurable impact: improved a slow query from seconds to under 200ms and increased onboarding completion by about 25%, while also building reusable platform primitives and scalable multi-tenant configuration systems.”