Vetted FastAPI Professionals

“Robotics engineer who built an educational/research robotic arm end-to-end, including IK/FK math, simulation-based validation, and ROS2 Humble integration with multiple teleoperation inputs (DualSense and Leap Motion). Also optimized a resource-constrained ESP32-S3 wireless maze robot to stream real-time video and sensor data over WiFi using TCP/HTTP with RTOS-based concurrency and binary struct packing.”

“Early engineering hire at The Coaching Market (B2B2C e-learning startup) who owned re-engineering core REST APIs (FastAPI) and shipping an enhanced Next.js UI, plus automating most deployments via CircleCI/Vercel. Strong in data-driven iteration (surveys/Jira/Slack + Google Analytics, A/B tests) and reliability/performance improvements with measurable impact (30% less downtime, 40% faster releases, 25% faster loads).”

“Sole software engineer for an ecommerce operation who ships and operates full-stack products end-to-end. Built a production AI voice agent integrated with Shopify and internal systems that now handles 4,000+ customer calls/month, plus a branded mobile app credited with $4M+ in sales and a self-serve returns/warehouse platform with strong React/TS UX and robust AWS operations.”

“AI/backend engineer who has built and scaled production LLM-powered SaaS features (document assistant + compliance review agent) on a Node.js/TypeScript + Postgres/Redis stack deployed to GCP Kubernetes. Demonstrates strong production reliability chops—async queueing, autoscaling, observability, and database tuning—with quantified wins (p95 latency -60%, query 4s to <200ms) and robust AI guardrails (strict RAG, schema validation, citations, HITL).”

“Built and deployed an LLM-powered automation pipeline that ingests voice and documents, transcribes/extracts key information into structured data, and routes it through backend workflows using Python/FastAPI. Uses n8n to orchestrate multi-step AI processes with validation, retries, and monitoring, and iterates with stakeholders via rapid demos to refine changing requirements.”

“Built and deployed a production LLM-powered recruitment chatbot that automates key recruiting steps (sourcing, candidate engagement, screening). Strong in agent orchestration with LangGraph, including guided graph-based workflows, context-aware routing, and reliability measures like clarifying steps plus human-in-the-loop evaluation.”

“Backend/data engineer with hands-on production experience building a FastAPI Python service on AWS for real-time AI workflows (Postgres/Redis, containers behind API Gateway) with strong reliability practices (JWT auth, timeouts/retries, health checks). Has delivered AWS infrastructure using Terraform + GitHub Actions across environments, built Glue ETL pipelines into Snowflake with idempotent recovery, and modernized legacy batch workflows via parallel-run parity validation and phased cutovers.”

“Frontend engineer (~4 years) who led an admin dashboard for potato farmers end-to-end, integrating real-time and historical weather data to drive disease-severity predictions. Built scientifically validated calculation pipelines (FAO Penman-Monteith, late blight models), rigorous Cypress/unit testing, and print/PDF-ready reporting, while optimizing performance and API load with debouncing, caching, and feature-flagged rollouts.”

“New-grad software engineer who built a real-world smart-city traffic camera/operations dashboard pitched to Peachtree Corners, GA, integrating Bosch cameras with an MQTT/Node-RED/InfluxDB pipeline and Grafana visualizations. Implemented rule-based alerts (including SMS dispatch for janitorial cleaning) and improved production reliability with queuing, redundancy, monitoring, and load testing, reaching ~99% message delivery. Actively seeking onsite, customer-facing roles with travel.”

“Built a Smart Resume Screening tool with a React frontend and a Python backend, owning most backend architecture and delivery. Implemented FastAPI endpoints for file upload and NLP/ML inference, created the end-to-end resume classification pipeline, logged predictions to a database for accuracy tracking, and deployed a Dockerized service optimized for low-latency, concurrent processing.”

“Early-career full-stack engineer who has not yet shipped a professional customer-facing product but has built sophisticated AI-driven systems in personal/academic work, including a multi-tenant AI knowledge base (async ingestion, pgvector semantic search, SSE real-time updates, knowledge graphs) and an AI-powered code review assistant designed to process thousands of jobs via Redis/BullMQ.”