Vetted State Management Professionals

“Built and productionized an AI agent-based in-app assistant at ISAFE to guide users through document workflows, piloting with a partner school district and then rolling out across districts. Combines hands-on LLM/agent debugging (logs, fallback rates, state/context tracking) with strong technical demos and sales enablement through live workflows and pilot programs (e.g., Osceola School District).”

“Built a production multi-cloud LLM-driven IT ticket automation system using LangGraph, Azure + Pinecone RAG, and an Ollama-hosted LLM on AWS, with Terraform-managed infra and PostgreSQL audit/state tracking for reliability. Also partnered with UW School of Medicine & Public Health students to deliver a glioma survival risk-ranking model, translating clinical feedback into practical pipeline improvements (imputation, site harmonization) and stakeholder-friendly visualizations.”

“Software engineer with payments-domain experience (Visa) building real-time transaction monitoring and analytics systems. Strong end-to-end ownership across Spring Boot/Kafka microservices, PostgreSQL modeling, and AWS/Kubernetes operations, plus React+TypeScript dashboards—focused on low-latency processing, secure APIs, and zero-downtime production releases.”

“Frontend lead who owned architecture and quality for TELUS’s Next Generation Sales Platform, building a modular React+TypeScript experience that scaled across wireline/wireless products and channels. Experienced in hardening UIs against unreliable backend integrations (API abstraction, retries/fallbacks, caching, logging) and delivering real-time dashboards via WebSockets, with strong CI/CD testing and blue-green release practices for high-stakes launches like Black Friday.”

“Frontend-focused engineer who built a full movie recommendation system from concept to production, comparing classic collaborative filtering with LLM-based recommendation approaches on AWS. Emphasizes scalable architecture, strict TypeScript data contracts, and high-quality Next.js/React UI patterns (defensive states, scoped state management, performance optimization) with disciplined QA and feature-flagged rollouts.”

“Built and shipped production systems spanning real-time operational dashboards and an LLM-powered internal documentation assistant using RAG (embeddings + vector DB). Demonstrates strong focus on reliability and iteration: implemented guardrails and evaluation loops (human review, hallucination tracking, regression prevention) and improved performance/scalability through query optimization, caching, and retrieval tuning.”

“Built and deployed a production agentic LLM knowledge assistant that answers complex questions over internal documents, APIs, and databases using a RAG architecture (FAISS/Pinecone) and LangChain/LangGraph orchestration. Emphasizes production-grade reliability and hallucination control through grounding, confidence thresholds, validation, retries/fallbacks, and full observability (logging/metrics/traces) with continuous evaluation and feedback loops.”

“Frontend engineer focused on marketing and analytics products, including a real-time multi-touch attribution dashboard. Uses Next.js (SSR/ISR) with React/TypeScript and Tailwind, and emphasizes quality at scale via automated testing, CI/CD (GitHub Actions), feature-flagged staged rollouts, and Mixpanel-driven iteration. Experienced modern state management patterns (React Query + Zustand) and performance tuning (code-splitting, dynamic imports, lazy loading).”

“Full-stack engineer with Bank of America experience modernizing a large-scale financial reporting platform. Built React frontends and Java/Spring Boot microservice APIs end-to-end, optimized data-heavy SQL performance (indexing/caching/pagination), and implemented an AI feature for forecasting and anomaly detection using Python/scikit-learn, with deployments supported on AWS.”

“Backend engineer who designed and evolved an AWS-based event-processing system in Python/PostgreSQL, achieving a 60% p95 latency reduction while improving reliability during traffic spikes. Led a zero-downtime migration from a monolithic Django app to FastAPI microservices using feature flags, strong testing, and cross-team coordination, with production-grade observability (Prometheus/Grafana/CloudWatch) and security (JWT/OAuth2, RBAC, Postgres RLS).”

“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.”

“AI/ML engineer who built a production RAG-based internal document intelligence assistant (LangChain + Pinecone) to let employees query enterprise reports in natural language. Demonstrated hands-on pipeline orchestration with Apache Airflow and tackled real production issues like retrieval grounding and latency using tuning, caching, and token optimization, while partnering closely with non-technical business stakeholders through iterative demos.”

“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.”