Vetted FastAPI Professionals

“Early-career full-stack builder who created an AI interview-prep platform used by 200+ students, tested it with a 25-student study group, and earned recognition through the CUNY Startup accelerator, including prize money and local college adoption. Has also shipped compliance-sensitive AI products in healthcare marketing and operational tools like invoice approval systems, showing unusual breadth across AI, UX, and backend systems.”

“Software engineer who built JobIntel, an end-to-end Python ETL pipeline integrating ATS data from platforms like Greenhouse using Scrapy and FastAPI. Stands out for production reliability work: designing async fault-tolerant architecture, optimizing PostgreSQL write-heavy upserts, and building a Prometheus/Splunk observability stack that cut debugging from hours to minutes.”

“Built and deployed an end-to-end fintech product, FinSight, for bank statement analysis and financial Q&A using a production-style RAG architecture. Stands out for combining FastAPI, OpenAI embeddings, FAISS, hybrid SQL/vector retrieval, and practical reliability work like chunking optimization, validation, and low-latency performance tuning.”

Naveen K

Screened

Mid-level Full-Stack Software Engineer specializing in AI agents and RAG workflows

San Francisco, CA3y exp

“Candidate is highly focused on AI-native software development, using tools like GitHub Copilot and OpenAI models within structured plan-code-review-test workflows. They stand out for designing multi-agent coding systems with planner, coder, and tester roles, and for applying tech-lead style governance through constraints, quality gates, and validation-first practices.”

“Backend-focused engineer with hands-on experience deploying AI-driven document processing and RAG-based workflows using Python, LangChain, FAISS, and REST APIs. Has owned projects from requirements through post-launch monitoring, including debugging production retrieval issues and building reliable pipelines for messy PDFs/scans and compliance-oriented document analysis.”

Naveen K

Screened

Mid-level Full-Stack Software Engineer specializing in AI-powered backend systems

San Francisco, CA2y exp

“Full-stack engineer with hands-on ownership of a real-time analytics and alerting dashboard built with React/TypeScript, Node.js, Kafka, Redis, and PostgreSQL. Also contributed to an internal LLM-powered support automation system, focusing on backend orchestration, RAG-based reliability, and Kubernetes deployment. Stands out for combining product-minded zero-to-one execution with strong distributed systems and AI integration experience.”

“Full-stack engineer with startup-style ownership across backend, frontend, and AI systems, spanning Java/Spring, React, Node/TypeScript, and LLM-powered retrieval. Shipped a workspace intelligence layer using LangChain, OpenAI, and Pinecone to paying customers, while also improving core product metrics like workspace creation success (+30%), latency (450ms to 280ms), and deployment cycle time (-40%).”

Dan Phelps

Screened

Staff Full-Stack Engineer specializing in SaaS, AI workflows, and biotech platforms

Remote13y exp

“Founding frontend engineer at a biotech startup who built a greenfield scientific web platform for designing and simulating genetic constructs. Brings rare experience combining modern React/TypeScript architecture, performance tuning, and interactive biological data visualization in a domain where correctness and data integrity are critical.”

“Built end-to-end AI products across fintech and healthcare, including a real-time loan risk prediction system and a patient feedback insights platform. Stands out for combining full-stack delivery, production ML/MLOps on AWS, and pragmatic human-in-the-loop safeguards; reported a 22% improvement in prediction accuracy.”

“Built both a fantasy sports analytics product and a privacy-sensitive AI assistant for therapists, showing range across consumer and healthcare use cases. Particularly notable for designing self-hosted, HIPAA-conscious LLM systems with RAG, structured outputs, observability, and human-in-the-loop guardrails for clinical workflows.”

Jaeseong Yoon

Screened

Intern-level Software Engineer specializing in full-stack and AI applications

Incheon, Korea0y exp

“Frontend-focused engineer who built Smart Place Analytics, a browser-based facility operations dashboard combining occupancy analytics, alerts, live monitoring, recommendations, and decision history. Stands out for pairing strong UI architecture in Next.js/TypeScript with hands-on browser media work like getUserMedia, canvas capture, throttling, and cleanup to make complex monitoring workflows feel stable and operator-friendly.”

“Backend/cloud engineer who built an AWS serverless IoT system that computes Bluetooth beacon locations from telemetry using heavy scientific Python (NumPy/SciPy/pandas) packaged as Dockerized Lambda, integrated with Java microservices and scheduled batch orchestration. Has deep AWS delivery experience (CI/CD with Code* tools, CloudFormation, cost controls) and has led high-severity incident response including CloudTrail forensics and infrastructure recovery after a compromised-keys crypto-mining attack.”

“Senior full-stack engineer with startup experience building multi-tenant B2B SaaS platforms for manufacturing and financial operations. Strongest in Python back-end development and React/TypeScript front ends, with hands-on AWS microservices, enterprise integrations like Siemens, and measurable performance gains including a 30% reduction in application load times.”

“Full-stack engineer with strong ownership across React, FastAPI, and PostgreSQL who has built real-time collaboration and analytics workflows end-to-end. Particularly compelling for high-growth AI product teams: they’ve also shipped a 0→1 AI-assisted dataset retrieval and summarization capability, balancing MVP speed with scalable architecture and post-launch performance tuning.”

“Backend/data engineer focused on high-scale, event-driven AWS ingestion systems (SQS/Lambda/EKS) processing millions of events per day, with strong reliability patterns (idempotency, DLQs, bounded retries) and deep observability using Datadog distributed tracing. Has delivered Terraform/GitHub Actions CI/CD and improved secret rotation via Secrets Manager + IRSA, plus Glue-based ETL with schema-evolution handling and Postgres SQL optimization (including JSONB/GIN indexing). Candidate is currently living outside the US and states they do not have US work authorization.”

“Backend/ML engineer who built a job-search automation SaaS using a modular Selenium ETL pipeline, rigorous testing/observability, and a cost-optimized two-pass LLM ranking approach. Has led high-integrity data extraction from messy multi-city PDF records (95% integrity) and managed modular production rollouts for a 20+ engineer team, with a strong security focus (deny-by-default, row-level access control) in an AI-assisted grading platform.”

“Backend engineer with health-tech AI platform experience who designed a modular FastAPI/PostgreSQL architecture supporting real-time user data and swap-in AI workflows. Has hands-on production experience with observability (CloudWatch, structured logging, LangSmith/LangGraph/LangChain tracing), secure auth (OAuth2/JWT, RBAC, RLS), and careful data-pipeline migrations using parallel runs and rollback planning.”

“Built and productionized an LLM-powered financial intelligence and forecasting platform at Northern Trust using a RAG architecture (LangChain + Hugging Face + FAISS) with end-to-end MLOps (Docker/Kubernetes, Airflow, MLflow). Emphasized regulatory-grade explainability (SHAP/Power BI) and hallucination control (retrieval-only grounding), achieving ~30% forecasting accuracy improvement and ~65% reduction in analyst research time, with sub-second inference and 95% uptime on EKS/AKS.”

“LLM/RAG practitioner building clinician-facing AI search and Q&A inside EHR workflows, focused on trust, latency, and safety (grounded answers with citations, PHI controls, encryption/audit logs). Demonstrated real-time incident response for production LLM systems (e.g., fixing a metadata-filter deployment regression to prevent irrelevant results/cross-patient leakage) and strong demo/enablement skills for mixed technical and clinical stakeholders; also shipped a multi-model RAG tool at OrbeX Labs with upload/search/audit features for day-to-day adoption.”

“ML/AI engineer who built and productionized an NLP system at PurevisitX, orchestrating end-to-end ML workflows with Airflow (S3 ingestion through auto-retraining) and optimizing for drift and low-latency inference. Also partnered with Citibank risk teams on a fraud detection model, translating results via dashboards and iterating thresholds based on stakeholder feedback.”

“Built an AI-based voice interviewer platform at 7C Lingo to automate early-stage candidate screening, owning the full lifecycle from architecture through deployment and weekly production iterations. Implemented a TypeScript/Next.js recruiter dashboard with a Flask/Postgres backend and AWS S3, plus modular services for transcription/analytics/session management using state-driven async workflows. Also created an internal Whisper-powered transcription and editing tool that evolved into a collaborative, versioned, live-transcription system.”

“Backend engineer who owned an order management API built with Python/FastAPI and PostgreSQL, integrating payment and shipping providers with strong reliability patterns (idempotency, async workers, retries/backoff, circuit breakers). Experienced deploying services to Kubernetes using a GitOps model with ArgoCD (auto-sync, self-healing, pruning, rollbacks) and building high-volume Kafka streaming pipelines. Has also supported phased cloud-to-on-prem migrations with a focus on security monitoring/SIEM log continuity.”

“Product-focused full-stack engineer (Spring Boot/Django + React/TypeScript) with deep experience building multi-tenant, enterprise workflow and supply-chain/order-tracking systems. Owned an end-to-end Workflow SLA Breach Prediction & Alerting feature integrating Azure ML for a cloud workflow platform used by ~10,000 enterprise users, and has hands-on AWS operations experience resolving real production latency/scaling incidents via query optimization and Redis caching.”

“Software engineer who built and open-sourced reusable React/Node.js modules (chat, auth, caching) from an AI education platform, emphasizing intuitive APIs and strong documentation. At TCS, improved a healthcare scheduling platform by diagnosing SQL/server bottlenecks and redesigning database + caching, cutting appointment load times by ~40% and reducing support complaints.”