Vetted MySQL Professionals

“Full-stack engineer who has shipped and operated generative-AI chat/QA features end-to-end, including a RAG-based pipeline with guardrails and cost/latency monitoring in production. Experienced with React/TypeScript + Node/Postgres architectures, Dockerized deployments to AWS (EC2) via GitHub Actions CI/CD, and building reliable ingestion/ETL systems with idempotency, backfills, and reconciliation.”

“Backend engineer with experience building an end-to-end civic tech AI platform that ingests city council meeting videos, transcribes them with Whisper, and enables natural-language Q&A via a LangChain/FAISS RAG pipeline. Demonstrated strong systems thinking by tuning retrieval for accuracy/latency/memory (cutting response time ~3s→1s and memory ~500MB→25MB) and by safely migrating an ERP from monolith toward services using dual writes, reconciliation, and idempotency to protect financial workflows.”

“Backend engineer from FoodSupply.ai who built and evolved a scalable restaurant/supplier product and order management platform using Node.js and REST APIs. Implemented a hybrid MySQL+MongoDB data architecture, optimized performance with Redis/Prisma, and led a phased migration with feature flags and a temporary sync layer to maintain data consistency. Strong focus on production security (OAuth2, RBAC, row-level security, AWS IAM) and reliability practices (testing with Pytest, Docker/AWS pipelines).”

“Built and deployed a production RAG-based multilingual analytics assistant for healthcare operations, enabling non-technical teams to query claims/EHR and risk metrics with grounded explanations. Demonstrates strong end-to-end LLM system engineering (retrieval tuning, re-ranking, hallucination controls, verification layers) plus workflow orchestration (Airflow/Composer/Step Functions) and stakeholder-driven iteration via prototypes and dashboards.”

“Software/product engineer who owns customer-facing internal platforms end-to-end, with deep experience building data pipeline health and data quality tooling (near-real-time alerting and ops dashboards). Strong in React/TypeScript + Python REST architectures and microservices with RabbitMQ, emphasizing reliability patterns (idempotency, DLQs, correlation IDs) and fast, safe iteration via feature flags, testing, and observability.”

“Graduate Research Assistant in Robotics at Arizona State University who built an end-to-end LLM-driven task execution framework enabling collaborative robots to convert high-level natural language instructions into safe, executable ROS actions. Implemented robust monitoring, failure detection, and automatic replanning, and addressed real-world issues like timestamp/frame-transform mismatches and heterogeneous robot interoperability using adapter nodes.”

“Early-career AI/LLM builder who created and deployed a multi-agent news analysis agent (Patrakarita) using CrewAI, coordinating researcher/analyst roles to turn noisy article URLs into structured, prioritized outputs (claims, tone, verification questions, opposing views). Strong focus on orchestration debugging and reliability evaluation, including measuring hallucination/redundancy and improving reasoning by refactoring pipeline sequencing.”

“Frontend engineer with experience leading an end-to-end website rebranding at Caseware, coordinating across product, design, DevOps, and backend teams while integrating CMS, third-party services, and backend APIs. Built SaaS dashboard interfaces in React + TypeScript and emphasizes quality and scale through unit/integration testing pipelines plus performance work like responsive image optimization, lazy loading, SSR, and React memoization.”

“Frontend lead for a consumer-facing social platform, owning architecture through release. Built scalable React/TypeScript systems (Redux Toolkit, Remix) with a shared Storybook component library and strong quality gates (CI, Jest/Cypress). Experienced modernizing legacy codebases incrementally with feature flags and shipping major dashboard features with staged rollouts and close QA collaboration.”

“Software engineer who builds workflow-centric products end-to-end, including a customer-facing module on the Trident AI content platform and an internal content workflow tool adopted as the default process. Strong in TypeScript/React + FastAPI architectures and in scaling event-driven microservices with RabbitMQ, emphasizing reliability (idempotency, DLQs) and observability (correlation IDs) to reduce outages and debugging time.”

“LLM workflow builder who has deployed a personalized GPT experience (including Delphi AI-based knowledge ingestion) and built a LangChain/LangGraph job-aggregation pipeline that ingests, normalizes/dedupes, filters, then uses an LLM to rank and summarize matches. Emphasizes production reliability with structured outputs, retries/fallbacks, metric-driven evaluation, logging/prompt versioning, and A/B testing, and collaborates with non-technical stakeholders through demo-driven iteration.”

“Built and production-deployed a Streamlit-based PDF RAG chatbot using LangChain (FAISS, embeddings, prompt templates) and OpenAI, optimizing Streamlit’s stateless behavior by caching vector DB + chat history to cut latency and API cost. Demonstrates a rigorous evaluation mindset (gold datasets, unit tests, LLM-as-judge, groundedness KPIs) and has experience communicating privacy/accuracy safeguards (RBAC, data masking, citations) to a non-technical client at Kalven Technologies.”

“Frontend engineer with React/TypeScript experience who has led end-to-end UI work on a fintech product (React frontend with NestJS APIs), emphasizing performance (virtualized rendering, memoization, lazy loading, profiling) and quality at scale (unit tests and TDD). Also built and iterated on a snooker sports app, simplifying UX through audience-focused design decisions and streamlined onboarding.”

“Frontend engineer with deep Shopify and React/TypeScript experience who has shipped real-time, high-concurrency products—most notably a live auction system on Shopify using pure JavaScript/Liquid with Pusher for instant bid updates. Also built a POS dashboard app with Redux Toolkit for real-time order/cart updates and delivered measurable storefront performance gains (~30–40% faster initial load) through modularization and asset optimization.”

“Frontend engineer who created an in-house React-like framework (“React-Wilcox”) enabling modern, event-driven UI components on extremely legacy browsers (as far back as 2002), including race-condition avoidance via batched state updates. Also does freelance work untangling AI/vibe-coded frontends for nontechnical founders, componentizing UIs and fixing routing/readability, and recently built a React+TS social app for martial artists with privacy-preserving location distance features.”

“Built and operated large-scale biodiversity/ecological research platforms, integrating 50+ heterogeneous global datasets into a unified BIEN 3 schema on PostgreSQL/PostGIS and improving data consistency by 35%. Strong production engineering background (Linux monitoring, CI/CD performance gates, Docker on AWS/Azure) plus applied AI work building a Python RAG system (0.90 precision) and halving latency with Elasticsearch.”

“Built an LLM-driven MLOps agent at SBD Technologies that automated an EV-charging prediction workflow end-to-end, integrating with real-time Kafka/FastAPI systems supporting 120K+ chargers at 99.99% event delivery. Addressed frequent schema drift by implementing SQLAlchemy/Flyway validation (60% reduction in drift issues) and deployed as Kubernetes microservices with GitHub Actions CI/CD; also has Airflow-based ingestion/crawling experience into Snowflake and stakeholder-facing delivery via a Fleetcharge PWA.”

“Backend engineer who owned the API layer for an AI trust/analytics dashboard (trust scores, stability checks, public verification endpoints) using Python/FastAPI and Postgres. Has hands-on DevOps experience deploying FastAPI and Node.js services to AWS Kubernetes with GitHub Actions + ArgoCD GitOps, plus Kafka-based real-time event streaming and careful staged migration practices (shadow traffic/dual writes, rollback planning).”

“GEODNET engineer specializing in edge-to-cloud, real-time GNSS data pipelines at global scale (thousands of heterogeneous base stations). Built deterministic-latency ingestion with RTCM/MSM normalization, jitter buffering, and firmware-aware parsing, and shipped production hotfixes using canary rollouts and deep observability. Also delivers customer-specific GNSS/RTK outputs via tested Python tooling (CLI/API) and collaborates on-site with operators to resolve firmware and network-driven issues.”

“LLM/agent systems builder who deployed a production hybrid router for immerso.ai that dynamically selects retrieval vs reasoning vs generative pathways, achieving an 82% factual-accuracy lift. Deep hands-on experience optimizing local Mistral 7B inference (4–5 bit GGUF quantization, KV-cache reuse) and building reliable RAG/agent workflows with LangChain/LangGraph/AutoGen across GCP Cloud Run and AWS (ECS/Lambda).”

“Built and deployed ForensicLLM, a local domain-specific LLaMA-3.1-8B model for digital forensic investigators using RAFT + RAG over 1000+ curated research papers, with citation-aware responses and rigorous evaluation (BERTScore/G-Eval). Deployed via vLLM and Docker and validated through a chatbot survey with 80+ participants; published at DFRWS EU 2025.”

“Full-stack developer who built an end-to-end billing/allocation/payment and reporting system used daily by a major film-industry union, including queuing-based check assignment, admin auditing, data cleanup tools, and an external reports portal. Also delivered a factory production scheduling/analytics app for a lock manufacturer, and typically implements APIs in C#/.NET with DTO shaping and pub/sub messaging for microservices consistency.”

“Engineering/CTO-style leader with hands-on architecture experience who has driven end-to-end modernization of a manual antiques auction operation—building centralized web-accessible data systems, digitizing historical records via OCR/freelancers, and defining profitability-focused KPIs with an eye toward predictive modeling. Emphasizes provider-agnostic, containerized SaaS architecture to avoid vendor lock-in and has experience scaling a small engineering team with ownership-based culture and lightweight processes.”

“Built and deployed a production generative-AI copilot at Tungsten that automates invoice/form extraction template creation, reducing weeks of manual model-building work. Combines fine-tuned LLMs (PyTorch/HuggingFace) with OpenCV layout grounding to reduce hallucinations, and runs an end-to-end Kubeflow-based MLOps pipeline with drift monitoring, canary releases, and automated retraining.”