Vetted Software Developers in New York

“Full-stack engineer/product owner who built and scaled a customer-facing job application portal (Skillbridge) using TypeScript/React and Spring Boot/MongoDB, optimizing search performance with indexing, caching (Redis), and payload/lazy-loading improvements. Also built an internal AI-driven analytics dashboard for Salesforce operations using OpenAI sentiment analysis, achieving 70% reduction in manual analysis and driving adoption through demos and iterative feedback.”

“New-grad/early-career engineer who led high-stakes modernization of a field-operations platform from Firebase to AWS using an incremental/dual-write strategy, achieving zero downtime and ~30–32% infra cost reduction while improving scalability. Also built and productionized an AI-native code assistant (LangChain + Pinecone RAG) with measurable online metrics and safety guardrails, and has experience working directly with CEO/CTO/CPO and embedded with customer teams to ship enterprise features quickly.”

“Master’s-level IoT course project that the candidate helped evolve into a research lab effort by “ROSifying” a soil-fertility detection rover (autonomous navigation within a GPS geofence, sensor fusion, and rover-to-base-station telemetry via NRF24 to a Raspberry Pi dashboard). Also built a ROS/Gazebo vision-based teleoperation system using a SigLIP hand-gesture model mapped to geometry_msgs/Twist, and improved stability by instrumenting and filtering a latency-prone perception-to-control pipeline.”

“Backend engineer who has shipped both high-throughput real-time systems and production LLM/RAG features. Built a database-free, local-first messaging service (Node/Express/Socket.IO) achieving ~1,500 msgs/sec at <25ms p95, and implemented a Go-based RAG recommendation pipeline with strict JSON/schema validation, catalog grounding, fallbacks, and eval loops that cut hallucinations to ~1–2% while reducing LLM costs ~60%.”

“Data engineer/software engineer who led an end-to-end ETL/ELT pipeline at Pearson processing millions of rows of student data nightly, including client-side data prep/validation, SFTP/API ingestion, staging-based SQL validation/transforms, and production loading. Built reliability features like configurable per-client validation thresholds, detailed reporting, concurrency throttling via a custom queue, and multi-source merge/backfill logic to keep nightly loads running even when sources fail.”