Vetted FastAPI Professionals

Nishantkumar Asodariya

Screened

Mid-level Supply Chain Analyst specializing in global logistics automation and forecasting

USA4y exp

“Built and shipped a production LLM-powered recruiting workflow that ranks resumes against job descriptions, generates evidence-based justifications, and finds "hidden fit" candidates using embeddings + RAG. Demonstrates strong production engineering around hallucination control, latency, and predictable LLM cost management (budget checks, top-K pruning, tenant caps), plus orchestration experience with Airflow/Prefect/Kubernetes and a structured evaluation/monitoring methodology for AI agents.”

“Full-stack engineer with recent JPMorgan experience building GPT-4-powered customer sentiment/feedback analytics products (Next.js 14 App Router + FastAPI + Postgres) and owning them post-launch with CloudWatch/Datadog observability. Also implemented Temporal-based transaction reconciliation workflows with strong reliability patterns (idempotency, retries, DLQ, versioning) and has prior high-scale healthcare dashboard experience at Optum.”

Harrishkumar Loganathan

Screened

Mid AI/Machine Learning Engineer specializing in FinTech and Generative AI

Remote, USA3y exp

“AI/ML engineer with hands-on ownership of enterprise LLM deployments at Freshworks, including a large-scale RAG chatbot serving 15,000+ users across six departments. Stands out for combining deep production engineering skills—AWS microservices, Kubernetes, observability, retrieval quality, and faithfulness evaluation—with strong cross-functional stakeholder leadership and prior large-scale fraud data pipeline experience at Socure.”

“Built an end-to-end AI resume tailoring system in Python/FastAPI with strong emphasis on reliability, validation, observability, and regression-based quality control. Also demonstrates thoughtful browser and workflow automation skills, especially around brittle UIs, resumable state, and turning ambiguous manual processes into production-ready automated pipelines.”

“ML/AI engineer with hands-on ownership of production GenAI and computer vision systems, spanning experimentation, deployment, monitoring, and iterative optimization. Stands out for shipping an enterprise RAG platform that cut manual review by 50% and a defect detection pipeline that reduced report generation from 15 minutes to under 1 second while maintaining high uptime and strong operational discipline.”

“Full-stack engineer with strong enterprise delivery experience across manufacturing and semiconductor use cases, owning deployments from discovery through post-launch support. Stands out for combining traditional product engineering with applied GenAI workflows and data pipeline reliability work, including a manufacturing app that reportedly saved a Fortune 500 customer about $6M and an AI chat panel adopted by 70% of pricing analysts.”

“ML/AI engineer with hands-on ownership of production computer vision, speech, and legal RAG systems. Notably improved a key-duplication CV pipeline enough to unblock commercial launch and remove specialist manual measurement, and also shipped a live Quran recitation detection feature for a product with 1M+ users.”

Thomas Le

Screened

Junior Full-Stack Software Developer specializing in web, mobile, and health tech

Remote2y exp

“Developer who uses AI as a productivity accelerator while maintaining strong ownership of code quality, security, and readability. Built an AI-powered planning tool during the PolyPrompt hackathon that transformed messy project requirements into structured tasks, timelines, and assignments, and has also led human teams through Veggie Rescue with a focus on user-aligned execution.”

“Candidate takes a disciplined, developer-in-the-loop approach to AI-assisted coding, using AI primarily for brainstorming, suggestions, and optimization while retaining full ownership of architecture and final code decisions. They also actively stay current on AI developments through research papers, communities, and emerging tools.”

“Founding engineer who has built web products end-to-end in startup settings, spanning FastAPI/React application development, auth, cloud deployment, and Kubernetes-based scaling. Particularly notable for designing custom GPU autoscaling for an AI-style recommendation product and later shipping workflow-driven healthcare support tooling using Temporal, Postgres, and modular backend logic.”

“Candidate brings practical GenAI engineering experience with a disciplined approach to AI-assisted development. They have designed lightweight multi-agent workflows for a RAG-based support copilot, including retrieval, relevance validation, response generation, and groundedness checks to reduce hallucinations.”

“Built and stabilized an AI-assisted document processing workflow for Possible Finance that supported underwriting without automating final loan decisions. Stands out for combining practical LLM integration skills with strong guardrails, validation, and fallback design in a financial workflow, delivering roughly a 35% reduction in document processing time.”

“LLM/agent engineer who shipped a two-stage AI recruitment screening platform at Foursquare that automated resume ingestion through behavioral assessment, delivering an 85% reduction in screening time across 5,000+ applications with auditability and confidence-gated decisions. Also built a multi-agent benchmarking framework using MCP tool interfaces and a RAGAS + LangSmith evaluation/observability stack, including async re-architecture that cut production latency by 50%.”

Shivoham Angal

Screened

Entry-level Backend Software Engineer specializing in AI and cloud systems

Remote, New York1y exp

“Backend-focused engineer who built a hackathon trading vault (AntiSwan) integrating the Polymarket CLOB client and applying the Kelly Criterion for allocation decisions. In an internship at StartupU, owned pre-launch monitoring by building Azure dashboards and Terraform/KQL-driven alerts with Microsoft Teams webhook routing, and previously automated a DynamoDB cross-region migration with integrity checks.”

“Engineering leader with hands-on AI/ML systems experience spanning production inference infrastructure and consumer-facing LLM products. At Jio, they led a 17-person AI features team and delivered measurable execution gains, including 40% faster deployments and 35% lower prediction latency, while also building an end-to-end RAG-based meal recommendation product using OpenAI and Gemini.”

“Operations-to-engineering builder who turned a real dispatcher pain point into a live fleet-tracking and routing prototype using React, FastAPI, WebSockets, and mapping/routing tools. Also building AI workflow-automation products for non-technical office users, combining deterministic validation, sandbox replay, and LLM-assisted workflow splitting. Especially interesting for teams that value scrappy zero-to-one product ownership and practical automation.”

Wei-Hsien Wang

Screened

Entry-level AI Engineer specializing in full-stack generative AI systems

San Jose, CA1y exp

“AI/full-stack product engineer who has shipped both user-facing and internal LLM products, from a photo-to-music recommendation app to an experimentation agent at Azazie. Stands out for combining modern app development with production-grade agent and GraphRAG systems, including a 500k+ email analysis platform and measurable impact like 3x experiment velocity, 75% setup-time reduction, and 65% faster task discovery.”

“Full-stack engineer who has built both a TypeScript-based HR/payroll platform and a production agentic AI support system end to end. Stands out for combining strong product judgment with deep LLM systems thinking: RAG architecture, confidence-based routing, evals, observability, and human-in-the-loop design in a greenfield environment.”

“Backend-leaning full-stack engineer with PayPal experience building payment orchestration, settlement, and merchant risk systems at production scale. Stands out for combining cloud-native AWS delivery, database/query performance tuning, and reliability work in event-driven microservices, including a monolith-to-microservices migration that doubled deployment frequency and cut incident response time by 40%.”

“Backend/platform engineer who has built and run production Python/Flask + Kafka microservices processing RFID and camera/RFID fusion streams for near-real-time retail cart updates at ~4–5M events/day. Strong in reliability/performance debugging (p99 latency, Kafka lag, Cosmos DB RU hot partitions) with measurable impact including ~30% database cost reduction, and has also shipped an end-to-end vulnerability scanning workflow with DynamoDB-backed state, idempotency, and robust retry/verification guardrails.”

“Backend/data platform engineer who recently owned an end-to-end large-scale financial data platform delivering real-time decision support for finance and operations. Has hands-on experience modernizing legacy batch pipelines into AWS cloud-native ELT with parallel-run cutovers, strong data quality controls (dbt-style tests, reconciliation), and measurable improvements in runtime, cost, and SLA compliance. Also builds scalable, secure FastAPI microservices using Docker, ALB-based horizontal scaling, Redis caching, and managed auth with Cognito/Supabase plus Postgres RLS.”

“Full-stack engineer who built an LLM-powered productivity web app (LifeOS) end-to-end with TypeScript/Next.js, Prisma, and Postgres, emphasizing fast iteration with stable API contracts and an isolated AI service boundary. Also built a security/compliance login-verification workflow at Medpace used within an internal admin portal for thousands of employees, and has AWS experience orchestrating batch GPU workloads with robust retry/idempotency patterns.”

“Built and deployed a production LLM/RAG clinical decision support system that enables real-time semantic search over unstructured EHR notes and delivers patient risk insights. Strong in healthcare-grade MLOps and compliance (HIPAA, PHI handling, encryption, RBAC, audit logs) and scaled embedding/retrieval pipelines using Spark/Databricks and Airflow. Partnered with clinicians via Power BI dashboards and explainability, contributing to an 18% reduction in patient readmissions.”

“Built and deployed an enterprise GenAI knowledge assistant over thousands of internal PDFs/reports using a RAG stack (GPT-4 + Hugging Face embeddings + vector DB) to reduce manual search and SME escalations. Uses LangGraph/LangChain to orchestrate modular agent workflows with relevance filtering and fallback handling, and applies rigorous evaluation (golden datasets, edge cases, A/B tests) with production monitoring metrics.”