Vetted Full Stack Developers in New York

“Backend engineer with strong performance and security instincts: built a Flask API for readability metrics with clean, testable modular design; optimized SQLAlchemy/Postgres to eliminate N+1 issues (800ms to 120ms). Also implemented an LLM-powered natural-language travel search using Claude Sonnet + Amadeus with RAG and anti-exploitation safeguards, plus multi-tenant isolation via Postgres RLS and Redis caching that cut search latency from ~20s to ~4–5s while reducing storage costs.”

“At Delta Airlines, built and shipped a production LLM-powered semantic search/troubleshooting assistant over maintenance logs and operational documentation using OpenAI embeddings and a vector database. Implemented hybrid ranking, query enrichment, and structured filters to improve relevance ~35% while optimizing latency via caching and vector tuning. Also designed a scalable Kafka + AWS (Lambda/SQS) ingestion pipeline with strong reliability/observability and an eval loop using real engineer queries and human review.”

“Full-stack engineer with cloud and GenAI experience who has owned production features end-to-end, including a reporting dashboard optimized from 14s to 5s using query/API refactoring and monitored via AWS CloudWatch. Also productionized an OpenAI-powered chatbot using LangChain with prompt design, guardrails, and evaluation via production logs and user feedback, and has led incremental legacy-to-microservices modernization with parallel run to avoid regressions.”

“Full-stack engineer with strong production ownership: built and operated a real-time transaction monitoring/fraud-alerting system using Java Spring Boot, Kafka, Docker, and AWS with CI/CD. Demonstrates metrics-driven operations (latency, stability, consumer lag, true/false positives) and reliability patterns for integrations (idempotency, retries/backoff, DLQs, reconciliation/backfills), plus modern React/TypeScript + Node/Postgres architecture experience.”

“LLM-focused engineer with end-to-end experience shipping an OpenAI-powered edtech teacher assistant into production, using Humanloop-driven prompt iteration, rigorous observability (Datadog), and A/B testing tied to real learning metrics (25% comprehension lift). Also led adoption-driving technical demos at SiriusXM (event-driven AWS Lambda/Kotlin/CDK pipeline cutting processing from 24 hours to seconds) and partnered with sales at Spresso.ai to close eCommerce SDK deals and boost activation from 40% to 85%.”

“Product-minded full-stack engineer who owns customer-facing products end-to-end and iterates quickly using MVP validation, feature flags, automated testing, and staged rollouts. Has built TypeScript/React systems with modular backends and designed microservices with async messaging (RabbitMQ), handling scale issues like ordering, retries, and idempotency. Also delivered an internal ops automation tool with a self-serve real-time workflow dashboard that reduced errors and drove rapid adoption.”

“Backend/AI engineer who has shipped production LLM systems end-to-end, including an AI request-routing service (FastAPI + BART MNLI + OpenAI/Gemini) that improved accuracy ~25% after launch via eval-driven prompt/category iteration. Also built an enterprise document intelligence/RAG platform on Azure (Blob/SharePoint/Teams ingestion, OCR/NLP chunking, embeddings in Azure Cognitive Search) with PII guardrails (Presidio), confidence gating, and scalable event-driven pipelines handling millions of documents.”

“Full-stack/AI engineer who has shipped production systems spanning real-time analytics dashboards and an internal LLM-powered knowledge assistant. Experienced with RAG pipelines (embeddings/vector DB, semantic retrieval, query rewriting) plus evaluation loops and guardrails, and builds observable Kafka-based data pipelines monitored with Prometheus/Grafana.”