Vetted PostgreSQL Professionals

“Front-end engineer with strong browser UI and accessibility instincts, including ownership of a complex responsive color/style picker for a major paint brand and foundational theming work for a SaaS CMS. Particularly notable for combining progressive enhancement, cross-browser rigor, and accessibility leadership to make polished interfaces work reliably across devices and user needs.”

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.”

“Geospatial/simulation specialist who built and shipped high-fidelity 3D visual databases for a DoD-used simulation/render engine, owning the full pipeline from dataset selection and ingestion (imagery/LiDAR/OSM/elevation) through performance testing and delivery. Implemented Python-based AI scripts to auto-detect terrain features and drive automated 3D scene population, and has experience with authoritative client-server synchronization (DIS) plus VR headset performance constraints (Varjo/Oculus/HoloLens at 90Hz).”

“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/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.”

“Frontend engineer who led the end-to-end UI for an internal employee catalog tool at Genetec, building React/TypeScript dashboards with complex search filters. Emphasizes tight product-owner feedback loops (weekly demos), Figma-based design alignment, and disciplined delivery practices using CI/CD, automated tests, and version tagging for rollouts/reverts.”

“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.”

“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.”

“University hackathon finalist (2nd place) who built CareerSpark, a production-style multi-agent career guidance app in 24 hours using a hierarchical debate architecture with a moderator/judge agent. Has startup internship experience at LiveSpheres AI using LangChain for multi-LLM orchestration, and demonstrates a structured approach to testing/evaluation (golden sets, integration sims, latency/accuracy KPIs) plus strong non-technical stakeholder communication.”

“Recent J&J intern who built a conversational RAG agent and led a shift from a monolithic model to a modular RAG workflow, cutting response time from several days to under a second by tackling data fragmentation, context retention, and embedding/latency optimization. Also worked on a large (7B-parameter) multimodal VQA pipeline for healthcare research and stays current via NeurIPS/ICLR and open-source contributions.”

“Robotics engineer with hands-on experience scaling a multi-vendor heterogeneous warehouse robot fleet, building a distributed “traffic manager” for collision avoidance and real-time rerouting using CBS/MAPF and DCOP-style negotiation. Strong real-time/safety-critical systems background (RTOS, deterministic lock-free multithreading) plus modern perception and simulation tooling (CNN-LSTM/transformers, CARLA/Isaac Sim, VIO/GTSAM, camera-IMU calibration). Startup-oriented and comfortable moving quickly from prototype to production.”

“Built a secure, on-prem/private GPT assistant to replace manual SharePoint-style search across thousands of policies/SOPs/engineering docs, using a production RAG stack (LangChain/LangGraph, FAISS/Chroma, PyMuPDF+OCR, vLLM). Implemented layout-aware ingestion (including table-to-JSON) and a multi-agent retrieval/generation/verification workflow with strong observability and compliance guardrails, delivering ~70% reduction in search time.”

“ML/AI practitioner with hands-on experience deploying models to production and optimizing for low-latency inference using pruning/quantization, with deployments on AWS SageMaker and Azure ML. Has orchestrated end-to-end ML pipelines with Airflow and Kubeflow (ingestion through evaluation) and emphasizes reproducibility via containerization and version-controlled artifacts, while effectively partnering with non-technical stakeholders using dashboards and business-aligned metrics.”

“AI/ML engineer who has deployed transformer-based NLP systems to production via Python REST APIs and Kubernetes on AWS/Azure, with a strong focus on latency optimization (p95), reliability, and scalable orchestration. Demonstrates pragmatic model tradeoff decision-making and strong stakeholder collaboration—improving adoption by making outputs more actionable with summaries, extracted fields, and confidence indicators.”

“Hands-on backend/AI engineer who solo-built two production Claude-based agent systems: an internal Slack RAG over Confluence/Jira/code/regulatory docs and a HIPAA/GDPR-compliant patient chatbot with embedding guardrails and expert-in-the-loop evals. Also architected a multi-region patient portal + microservices platform with Terraform/CI-CD and federated gateways, delivering major onboarding automation and strong reliability wins (PgBouncer, chaos/perf testing).”

“Backend engineer with American Express experience maintaining an internal Python/Flask rewards simulation microservice used by product analysts and QA. Demonstrated strong performance and scalability work: moved batch simulations to Celery, added Redis caching to cut DynamoDB latency, and tuned Postgres/SQLAlchemy queries with EXPLAIN ANALYZE and composite indexes (bringing API responses under ~200ms by queueing jobs). Also has experience integrating ML via Flask-based model-serving APIs (scikit-learn/LightGBM packaged with joblib) and designing multi-tenant data isolation and tenant-specific configuration systems.”

“Built and deployed an end-to-end product that brings a research-paper approach into production for large-scale time-series clustering, with attention to partitioning, latency, and scalability. Also designed a Python-based backend validation service (comparing outputs to database ground truths) and handled production reliability issues by reproducing dataset-specific crashes and hardening corner-case behavior with client-friendly errors.”

“Backend engineer working on high-volume insurance claims intake systems who shipped a production GenAI document-classification capability in Spring Boot microservices. Emphasizes reliability in LLM systems (strict schemas, confidence thresholds, monitoring, and manual-review fallbacks) and runs evaluation loops with labeled historical documents to drive prompt/validation improvements and reduce manual review.”

“Backend/ML platform engineer in the insurance domain who built and shipped an AI-driven risk scoring/fraud detection service for underwriting. Runs containerized .NET Core and Python inference services on Azure (AKS + GPU nodes) with Terraform/ARM and Azure DevOps CI/CD, and has hands-on experience improving reliability under peak load plus implementing production AI guardrails (drift monitoring, fallbacks, human review, audit logs).”

“Cloud/DevOps engineer (6+ years) with healthcare domain experience who has owned production AWS systems end-to-end—building real-time data pipelines and an admission forecasting ML service delivered via API and Tableau. Led EMR modernization from on-prem/VMs to containerized AWS using phased migration and blue-green deployments, achieving ~99.5% uptime while cutting on-prem footprint ~30% and driving major automation gains (up to ~90% manual work reduction).”

“Full-stack engineer who built a cloud storage app feature (file upload/management) with Next.js App Router + TypeScript and owned post-launch improvements. Also has internship experience building a geospatial AI chatbot: designed Postgres/PostGIS data models and optimized spatial queries, and implemented an LLM workflow orchestrated with LangChain/LangGraph plus a RAG pipeline grounded in OpenStreetMap data to reduce hallucinations.”

“Full-stack engineer with startup experience who owned an end-to-end rebuild of a production analytics page at VideoNest (Next.js/TypeScript frontend, FastAPI/Python backend, Postgres), including third-party data ingestion/sync and query/index optimization; the feature reached 2,500+ users and received positive feedback from large clients. Also built a habit/community mobile app (Celeri) with near-real-time step updates using polling and UI optimizations like pagination and selective re-rendering.”