Vetted Redis Professionals

“Full-stack engineer with about 3 years of experience who is deeply hands-on with AI-assisted development and agentic systems. Built TubeAgent using LangChain, Ollama, FAISS, and Llama 3, and has demonstrated measurable impact by cutting review time by 90% and reducing deployment time from 30 minutes to under 5 minutes at NC State. Combines practical experimentation with strong architectural thinking around resilient, composable AI systems.”

“Backend/full-stack engineer who has built AI-powered search and workflow systems in production, including a semantic resume-matching platform for recruiters and internal security data dashboards at ReliaQuest. Stands out for combining modern AI tooling with pragmatic reliability, performance tuning, and strong product intuition in ambiguous environments.”

Vincent Fearing

Screened ReferencesModerate rec.

Junior Full-Stack Engineer specializing in FinTech systems

Austin, TX1y exp

“Full-stack engineer with deep experience in high-stakes integrations: owned end-to-end fintech payment notification/installment tracking at an early-stage startup (FastAPI/React/AWS), including multi-environment routing for live banking partners and reliability patterns like idempotency and retries. Also built a Coachella partner ticketing platform (React/TS/Node/Postgres) with strong concurrency controls and zero-downtime migrations, and previously delivered media-asset ETL/file-sharing automation at Sony Pictures using Frame.io with checksum-verified transfers.”

“Backend/data engineer who has maintained high-traffic FastAPI microservices and delivered a hybrid AWS serverless+containers platform using Terraform and GitHub Actions, with secrets managed via Secrets Manager/SSM. Also led modernization of a mission-critical 10,000+ line SAS financial reporting engine into Python microservices and built AWS Glue ETL pipelines feeding a centralized data lake.”

“AI/ML engineer with CVS Health experience deploying production LLM systems in regulated healthcare settings, including a large-scale RAG solution (1M+ documents) built for compliance-grade, auditable policy/regulatory Q&A with strong anti-hallucination controls. Also delivered an NLP summarization system for physician notes/case narratives by partnering closely with non-technical care operations stakeholders and iterating via prototypes, dashboards, and feedback loops.”

“Built and shipped a production LLM-powered grading platform that automates rubric-aligned scoring and feedback, with strong guardrails (RAG grounding, structured JSON, validation/retries) and operational rigor (metrics, drift monitoring). Experienced using CrewAI to orchestrate multi-agent workflows end-to-end and validating quality via gold-set benchmarking against human graders with regression testing on every prompt/model change.”

“Backend-leaning full-stack engineer in lending/financial services (Kotak Mahindra Bank Autos360; currently at Ally Financial) working on Spring Boot microservices with React dashboards. Has built reliability improvements for credit-bureau integrations (Experian) and created an internal monitoring/reporting platform that aggregates metrics/logs/ETL across services, cutting troubleshooting by ~40%.”

“Open-source JavaScript library contributor/maintainer focused on performance and usability—uses profiling and user feedback to optimize large-dataset processing and modernize abstractions. Refactored a nested-callback event handling system into an observer-pattern dispatcher with batched event queues, reducing CPU usage and improving maintainability; also handles community-reported crashes by reproducing issues, fixing memory leaks, and updating docs.”

“Backend engineer/technical lead with experience building and operating real-time blockchain analytics systems at Merkle Science. Owned high-traffic Django/DRF services and Kafka streaming pipelines processing millions of events daily, with deep focus on performance (N+1 fixes, indexing, caching) and reliability (DLQs, retries, monitoring). Also led containerization and Kubernetes/GitOps-style CI/CD on Google Cloud, including a migration off Google App Engine to reduce cost and improve scalability.”