Vetted MySQL Professionals

“Candidate explicitly stated they do not have production agentic/LLM or generative AI experience, aside from spending a few hours becoming familiar with the process. Compensation expectation stated as 225,000.”

“Full-stack/product-minded engineer with strong React/TypeScript depth who has owned systems end-to-end, from UI architecture to backend services and data design. At Qualcomm, they built both a telemetry dashboard and an ML model drift monitoring platform for 20+ edge models, including post-launch tuning that cut false positives by 60%. They also demonstrate 0→1 startup execution by solo-building a production RAG document Q&A platform with JWT auth, Stripe gating, and sub-300ms retrieval.”

“Engineer working on high-throughput financial systems who uses AI pragmatically to accelerate development without sacrificing design ownership, correctness, or compliance. Particularly interesting for teams building regulated, real-time platforms: they have hands-on experience integrating fraud detection models into microservices, handling transaction ingestion, scoring, decisioning, and throughput-sensitive asynchronous workflows.”

“Senior engineering executive with 8+ years leading large-scale SaaS modernization across AI, compliance, ecommerce, streaming, IoT, and travel. Has led a 150+ global engineering org, modernized seven cloud-native platforms for a $400M business, and consolidated travel systems processing $1B+ annually while staying hands-on in architecture, incident response, and AI integration.”

“Built Complyma's workspace intelligence layer end-to-end, shipping a zero-to-one semantic search and RAG assistant used by paying customers during an internship window. Combines full-stack product execution with strong multi-tenant security thinking, delivering measurable gains including 60% faster retrieval across 50K+ records and 30% higher workspace creation success.”

“At KPMG, built and productionized a secure RAG-based LLM assistant that lets business and risk stakeholders query data warehouses in natural language, reducing dependence on data engineers for ad-hoc analysis. Demonstrates strong production rigor (Airflow orchestration, CI/CD, containerization), retrieval/embedding tuning (rechunking, semantic abstraction for structured data), and reliability controls (confidence thresholds, refusal behavior, monitoring and canary evals).”

“Customer-facing technical professional with experience supporting LLM/agentic-style workflows and complex integrated systems (APIs, backend logic, databases). Partnered with sales/customer teams at Radix Health to onboard new clients in phased prototypes, translating non-technical requirements into technical scope and implementing core product changes to tailor the appointment-booking solution for providers.”

“Full-stack engineer with experience at Vanguard, PNC, and Humana building customer-facing investment/banking flows and internal ops tools using Angular/React/TypeScript with Spring Boot microservices. Strong in shipping time-sensitive changes safely via automated testing/CI (JUnit/Mockito, Jenkins, SonarQube) and in operating event-driven microservices with Kafka (idempotency, retries, correlation IDs). Improved internal tool adoption by responding to ops/support feedback with query optimization and clearer search results.”

“Full-stack engineer with hands-on experience building a large-scale healthcare claims and provider-enrollment system end-to-end (React frontend, Spring Boot microservices, PostgreSQL on AWS). Optimized high-volume claims processing (millions of records/day) using indexing/pagination and asynchronous workloads via AWS Lambda/Kafka, and deployed containerized services with Docker/Jenkins on AWS.”

“ML/GenAI engineer with production experience building an LLM-powered governance layer that summarizes verified drift/performance signals into validation reports and release notes, designed for regulated environments with de-identification and non-blocking fallbacks. Strong Airflow-based orchestration background across healthcare and finance, integrating Databricks/Spark and MLflow for scalable retraining/monitoring. Demonstrated ability to partner with non-technical healthcare operations teams to deliver actionable risk-scoring outputs via dashboards and automated reporting.”

“Developer with experience across Drupal and Java/Spring Boot applications using React/jQuery for UI and API-driven features. Has handled production issues by tuning reverse proxy timeouts for login problems and troubleshooting data pipeline inaccuracies by fixing database queries, with a focus on performance and careful verification before changes.”

“Backend engineer who built Flask-based microservices for a high-throughput risk engine, using Kafka for streaming decoupling and Redis for low-latency caching, with PostgreSQL + Cassandra for mixed relational and time-series needs. Has hands-on experience productionizing ML inference (Azure OpenAI/TensorFlow) behind REST APIs with async queues, batching, and caching, plus multi-tenant isolation via schema separation and RBAC with per-tenant rate limiting.”

“Platform-minded JavaScript/TypeScript engineer who has maintained and evolved shared NPM-distributed libraries at Capital One and HCA Healthcare, treating internal packages like open source (issue triage, PR reviews, roadmap, docs). Known for methodical debugging and performance work—e.g., diagnosing latency spikes via load testing/instrumentation and redesigning middleware to avoid redundant parsing—paired with strong developer enablement through examples and migration notes.”

“Small-team builder of Promethium’s “Mantra” next-gen agentic text-to-SQL engine, using vector DB + LangGraph tooling and SQL validation/evaluation to improve query accuracy. Experienced in diagnosing production LLM workflow failures via LangSmith traces and in running hands-on developer workshops and pre-sales POCs with live debugging and real customer data.”

“Maintained and improved a set of JavaScript packages at JPMorgan, including a major refactor of an event-emission layer used in LLM streaming that delivered ~20% runtime speedup with no API changes. Known for a measurement-driven performance approach (profiling + simplification + validation), strong backward-compatibility discipline, and documentation that accelerates internal adoption and reduces support questions.”

“Backend engineer who built a containerized Flask service powering an engineering metrics dashboard by syncing GitHub and Jira data into PostgreSQL, with strong emphasis on schema design, query performance, caching, and background processing. Has hands-on experience with SaaS multi-tenancy (tenant scoping + Postgres RLS) and integrating AI/ML inference via separate model-serving services (FastAPI + TensorFlow Serving) and external APIs (OpenAI/Hugging Face/PyTorch).”

“Built production LLM systems including a real-time customer feedback analysis and workflow automation platform using RAG and multi-agent orchestration with confidence-based human escalation, addressing privacy and legacy integration challenges. Also automated ML operations with Airflow/Kubernetes (e.g., daily churn model retraining) cutting retraining time to under 30 minutes, and demonstrates a rigorous testing/monitoring approach plus strong non-technical stakeholder collaboration.”

“Healthcare-focused data engineer/ML practitioner with experience at Lightbeam Health Solutions and Humana building production entity-resolution and semantic similarity pipelines across EMR, lab, and claims data. Uses NLP/ML (spaCy, scikit-learn, BioBERT/LightGBM) plus Snowflake/Airflow and vector search (Pinecone) to improve linkage accuracy (reported 90%) and semantic match quality (reported +12–15%), while reducing manual cleanup by 40%+.”

“Solo-built and launched an AI logo generator SaaS in ~2 months using React/Next.js/TypeScript with managed auth and payments, deploying via Vercel/GitHub CI/CD. Also has hands-on AWS production experience running containerized services with Terraform-managed multi-environment infrastructure and strong reliability patterns for integrations/pipelines.”

“QA automation engineer with end-to-end ownership of a loan-processing automation suite spanning UI, API, and database validations (Selenium/Playwright/TestNG/REST Assured; Java/Python). Caught and prevented high-impact financial defects (e.g., risk-calculation rounding errors) through CI-driven nightly regressions and API-to-DB checks, and has implemented maintainable Cypress patterns with flake reduction plus GitLab CI gating and Allure reporting.”

“Software engineer with Citi Bank experience building real-time fraud validation/scoring for loan processing, spanning Spring Boot microservices and a FastAPI Python service secured with OAuth2/JWT. Delivered React/TypeScript operations dashboards and deployed containerized services via Docker/Kubernetes with Jenkins CI/CD, while also tuning databases (Oracle/Postgres) and handling high-volume latency/scaling issues using ELK, caching, and autoscaling on AWS.”

“Providing technical guidance to a small team exploring a biomedical startup focused on earlier disease detection, including for remote/underserved areas. The venture is in ideation with initial research completed and is moving toward prototyping while exploring initial investment/support.”

“Cloud/Kubernetes-focused production engineer with experience running 99.95% uptime platforms across AWS/Azure/GCP. Strong in incident response and performance troubleshooting (including a 30% MTTR reduction), and in building secure CI/CD and Terraform-based IaC for AKS/GKE microservices with robust change controls and rollback practices. Notably does not have direct IBM Power/AIX/VIOS/HMC or PowerHA/HACMP ownership.”