Vetted ETL Professionals

“Data engineer focused on reliability and data quality, owning end-to-end pipelines processing ~100k–300k records/day. Implemented robust validation and monitoring that cut reporting issues by ~30%, and built stable external data collection with anti-bot measures, backfills, and schema-change detection while maintaining backward-compatible internal data services.”

Wilson Harron

Screened

Director-level AI/ML & Computer Vision Engineer specializing in robotics and multimodal AI

Los Angeles, CA15y exp

“Candidate is not currently pursuing entrepreneurship (no business plan and no capital raised) and is not familiar with the VC/accelerator landscape. They show pragmatic, problem-first thinking about evaluating startup ideas—prioritizing real customer pain points and the quality of the founding team—and are open to working for others rather than founding "at all costs."”

“Built and owns the full ProteinMenus AI pipeline end-to-end, spanning the iOS client, FastAPI backend, Gemini integration, Firestore, and Cloud Run deployment. Strongest signal is full-stack product ownership in an AI-driven consumer workflow, including monetization logic via an atomic credit system and architecture choices optimized for fast iteration after launch.”

“Marketing analytics candidate with experience at WPP and on a global Coca-Cola campaign, focused on turning messy multi-platform media data into trusted reporting and decision systems. They combine hands-on SQL/Python pipeline building with stakeholder KPI alignment, and cite a 22% improvement in media effectiveness plus faster budget reallocation through daily automated reporting.”

“Analytics professional with higher education experience at the University of Dayton, focused on turning inconsistent operational data into standardized metrics and recurring dashboards. They combine SQL, Python, and Power BI to automate reporting, improve data integrity, and reduce manual reporting by 30%, with outputs adopted in semester planning and cross-department performance tracking.”

“Built production LLM/RAG systems during a UPS internship, including a shipment knowledge agent used across 15+ hubs worldwide and a multi-agent PDF RAG workflow. Stands out for combining hands-on enterprise integration with rigorous evaluation, hallucination reduction, and efficient fine-tuning techniques like LoRA.”

“Built both traditional internal tooling and LLM-powered systems during an internship, including a React/Python/AWS calculator onboarding platform and a production-style ROS2 RAG assistant over 10K+ documents. Stands out for combining full-stack delivery, stakeholder coordination, and practical AI reliability work like retrieval tuning, source-grounded answers, and low-confidence fallbacks.”

Supreet Purthpli

Screened

Mid-level AI/ML Software Engineer specializing in cloud-native MLOps and FinTech

San Francisco, CA4y exp

“Software engineer with JPMorgan Chase experience delivering end-to-end fintech features (Next.js/React/Node/Postgres on AWS) and measurable performance gains. Built and productionized an AI-native credit decisioning workflow combining LLMs, vector retrieval, and a rules engine with strong governance (bias checks, auditability, human-in-loop), improving precision and cutting underwriting turnaround time by 40%.”

Ajay Desai

Screened

Mid-level Full-Stack Software Engineer specializing in FinTech and backend platforms

USA4y exp

“Built an AI-native legal research platform that automated analysis across 100,000+ dense legal documents, combining LLM workflows, async backend architecture, and conversational retrieval in production. Also brings cross-domain experience in investment-analysis agents and healthcare claims/billing systems, with a strong emphasis on reliability, deterministic orchestration, and safe handling of messy operational data.”

“Full stack Python developer who actively integrates AI coding assistants into day-to-day engineering work, including code generation, debugging, testing, and documentation. Has also coordinated multi-agent workflows across backend, frontend, testing, and code review, showing an applied, productivity-focused approach to AI-enabled software delivery.”

“Backend/AI engineer who has owned production agentic systems end-to-end, including a CRM-integrated multi-agent financial workflow at Wow Payments that cut latency by 83% and achieved 98% uptime. Also built an AI real estate product ('Site IQ') by turning vague stakeholder goals into a geospatial autonomous agent using RAG, rapid prototyping, and tight validation layers around GPT-4 outputs.”

“Frontend-focused candidate with hands-on experience building React and TypeScript dashboards for searching, filtering, and analyzing large datasets in real time. Demonstrates practical performance tuning skills using React DevTools, memoization, debouncing, and pagination, and has also built a Mapbox-based location data dashboard with interactive markers and popups.”

“Engineering/technology leader who stays hands-on while driving executive-level roadmap execution, with deep experience modernizing cloud-based LIMS/LIS platforms and building AI-driven lab analytics. Led a monolith-to-microservices cloud migration with containerization and CI/CD, and delivered a reported 30% reduction in lab turnaround time while strengthening compliance.”

“Full-stack product engineer who has built both operational systems and enterprise AI copilots in production. They owned an AI-powered inventory platform end-to-end, driving a 45% drop in stock issues, and also shipped a Microsoft Teams-based HR/IT copilot using RAG and workflow automation that reduced repetitive support queries by roughly 30%.”

“Built and owned Citigroup's ASTRA AI-powered test case generation platform end to end, from full-stack product experience to multi-agent LLM orchestration and RAG infrastructure. Drove test coverage from 40% to 95%, cut generation time from hours to minutes, and scaled the feature to 300+ daily users across 32 enterprise projects with sponsorship from Citi's CIO and Head of Engineering Excellence.”

“Built and owned an AI-powered real-time financial fraud detection and monitoring platform end-to-end, spanning product decisions, backend architecture, frontend dashboards, deployment, and production support. Their work scaled to 120M transactions/day and materially improved fraud detection accuracy from 78% to 94%, showing rare breadth across distributed systems, observability, and React-based operational analytics.”

“Built and deployed a compliance-sensitive LLM pipeline that extracts rebate logic from hospital–supplier medical contracts, using multi-layer redaction (regex/NER/dictionary), schema-validated structured outputs, and secure placeholder reinsertion. Hosted models on Amazon Bedrock to avoid retraining on sensitive data and improved both accuracy and cost by splitting the workflow into a lightweight section classifier plus a fine-tuned extraction model, orchestrated with LangChain and evaluated via layered, test-driven agent assessments.”

“Backend/data engineer who builds production FastAPI microservices and AWS serverless/Glue pipelines for SMS analytics and marketing segmentation. Led a legacy batch modernization into modular services (FastAPI + Glue/Athena + ClickHouse) using shadow-mode parity checks, feature flags, and incremental rollout. Demonstrated measurable performance wins (12s to sub-second SQL; ~40% CPU reduction) and strong incident ownership with proactive schema-drift prevention.”

“Built and deployed a production LLM-powered calorie-counting chatbot that turns plain-English meal descriptions into normalized food entities, quantities, and calorie estimates using a hybrid transformer + rule-engine pipeline. Emphasizes reliability with schema/constraint guardrails, confidence-based routing (including embedding similarity search fallbacks), and strong observability/metrics (hallucination rate, calibration, latency, cost). Partnered closely with nutritionists to encode domain standards into mappings and validation logic.”

“AI engineer who has shipped production LLM systems across customer service and marketing use cases—building a RAG app on Azure OpenAI and speeding retrieval with Redis caching tied to Okta sessions. Also implemented a LangGraph multi-agent workflow that pulls image context from Figma to generate structured HTML marketing emails, adding a verification agent to improve image-selection accuracy while optimizing solution cost for business stakeholders.”

“Backend/platform engineer who owned a real-time customer analytics microservice stack in Python/FastAPI with Kafka streaming into PostgreSQL, including schema enforcement (Avro) and high-throughput optimizations. Strong Kubernetes + GitOps practitioner (EKS/GKE, Helm, Argo CD) who has handled CI/CD reliability issues with automated pre-deploy checks and rollbacks, and supported major migrations (on-prem to AWS; VM to EKS) with blue-green cutover planning.”

“Built and deployed a production LLM-powered clinical note summarization system used by care managers to speed review of 5–20 page unstructured medical records. Implemented safety-focused validation (prompt constraints, rule-based and section-level checks, human-in-the-loop) to reduce hallucinations while maintaining low latency and meeting privacy/regulatory constraints, integrating via APIs into existing clinical tools.”

“Full-stack engineer specializing in Java Spring Boot microservices and React, with hands-on ownership of a merchant dispute management platform (security via RBAC/JWT, significant performance gains through SQL execution-plan-driven tuning and UI refactors). Also has experience at JPMorgan Chase optimizing high-volume financial-data services with API efficiency, caching, and async processing.”

“Full-stack developer who built an end-to-end Hotel Management System using React and Spring Boot with MongoDB and AWS. Has hands-on experience debugging API/data-fetching issues with Postman and validating results against the database, plus exposure to handling large data workloads with chunking and monitoring via Grafana/Tabula.”