Vetted FastAPI Professionals

“Built and deployed a GenAI-powered PhD career intelligence platform at NYU that maps academic backgrounds to career paths and converts long academic CVs into job-ready resumes. Stands out for treating LLM systems as structured production pipelines—combining NLP extraction, embeddings, orchestration, and AWS deployment—to improve recommendation quality and cut resume preparation time by 70%.”

Kavya Aitagoni

Screened

Junior Full-Stack Engineer specializing in web platforms and live events

San Francisco, CA2y exp

“Full-stack product engineer with experience shipping user-facing web products end-to-end, including an event analytics dashboard and checkout improvements at Eventbrite. Stands out for combining frontend polish, backend reliability, and production-minded practices like idempotent APIs, query optimization, CI/CD, logging, and monitoring to improve conversion and reduce engineering dependency.”

“Built and deployed a production RAG-based internal knowledge assistant that let analysts query company documents in natural language, using LangChain/LangGraph with Pinecone and a FastAPI service for integration. Emphasizes reliability in production through hallucination mitigation (retrieval tuning + prompt guardrails) and measurable evaluation/monitoring (accuracy, latency, task completion, hallucination rate), iterating based on user feedback.”

“Built an enterprise RAG-based document intelligence system at Freddie Mac for regulatory and financial documents, helping analysts cut search time from hours to minutes while improving retrieval accuracy by ~30%. Stands out for combining LLM product delivery with compliance-grade auditability, production monitoring, and scalable Python/FastAPI service design.”

“Built and deployed an AI-driven expense categorization workflow integrating OpenAI API and PGVector to automate general ledger coding. Stands out for combining LLM/embedding architecture with finance operations context, stakeholder-facing deployment ownership, and measurable impact of roughly 30%+ reduction in manual coding effort.”

“Analytics-focused candidate with hands-on experience across SQL data preparation, Python modeling, chatbot evaluation, and engagement metric design. They’ve worked on projects ranging from real estate deal analysis using 17,500+ Zillow listings to unemployment modeling, YETI chatbot performance analysis, and a generative-AI museum exhibit focused on participation and retention.”

“Growth-focused B2B SaaS operator with hands-on experience improving enterprise adoption for a cloud governance and FinOps platform. They combine customer discovery, ROI-driven messaging, automation, and funnel instrumentation to improve conversion and handoffs, citing an 18% lift in enterprise adoption and roughly $200K-$3M in influenced pipeline.”

“Built an internal RAG assistant for financial documents using FastAPI, OpenAI APIs, and vector search, improving document search speed and reducing manual effort for the business team. Stands out for a pragmatic approach to AI engineering: uses AI heavily for productivity, but keeps human judgment central and has designed retrieval, validation, and summarization workflows end-to-end.”

“AI and platform engineer with 5 years of experience who built a production knowledge assistant for Verizon end-to-end, from architecture through deployment, monitoring, and incident hardening. Stands out for combining modern LLM/RAG systems with enterprise-grade rigor, including validation layers, observability, versioning safeguards, and measurable impact on technician productivity and retrieval quality.”

“Currently building a DBC (Digital Birth Certificate) agentic AI system to speed root cause investigation for quality issues at their company. They bring hands-on experience designing and leading multi-agent workflows, including orchestrator/root-agent patterns, evaluation agents, clarification agents, and practical guardrails for hallucination, bias, and rate-limit management.”

“Developer with a pragmatic, disciplined approach to AI-assisted coding: uses tools like Copilot, ChatGPT, and Gemini to speed up debugging, optimization, unit testing, and documentation while maintaining ownership of design and code quality. Interested in expanding from single-agent workflows into multi-agent setups for larger coding tasks and stays current through hands-on use and AI ecosystem updates.”

Yash Priyadarshi

Screened

Junior Software Engineer specializing in distributed systems and cloud infrastructure

Bengaluru, India2y exp

“Backend/distributed-systems engineer who built a Golang distributed key-value store on AWS using Multi-Paxos, WAL, and non-blocking gRPC replication (cutting write latency ~40%) and proactively addressed tricky failure modes like leader-election livelock. Also developed a Python/Kubernetes cost-optimization scaling engine deployed with Helm/Terraform, delivering ~$40K annual savings while sustaining 99.99% uptime, and drives contract-first API development (OpenAPI/Swagger) to speed frontend integration.”

“Early-career software/AI intern with startup and hackathon experience who blends backend engineering with product communication and user-feedback-driven iteration. Worked in a fast-paced SaaS environment at Airmeet and has experience pitching technical products, refining onboarding/workflows, and thinking beyond pure implementation toward adoption and growth.”

“AI/full-stack engineer in gaming analytics who joined Omnic.ai at a 2-person stage, helped grow with the company, and built both backend and frontend for real-time gameplay analysis products. He combines computer vision production experience with LLM/RAG systems work, and has already led 4 employees while shipping 12 models in a fast-moving startup environment.”

MohmmadHussain Syed

Screened

Mid-level Full-Stack Engineer specializing in customer-facing web platforms

USA4y exp

“Full-stack product builder who described owning an AI-powered journaling platform end to end using React/Vue, FastAPI, Supabase, PostgreSQL, and Hugging Face APIs. Also shipped a customer-facing document upload feature for First National Bank by solving micro frontend integration issues with web components, and has built internal tooling such as a GitHub PR review app.”

“Frontend-leaning full-stack engineer with strong ownership in network observability and analytics products, including BT Group's SMARTS platform and SSpain.ai at Texas A&M. Stands out for building data-dense, near-real-time dashboards and shaping products end-to-end across React/Angular frontends, FastAPI backends, PostgreSQL, AWS, and even React Native mobile surfaces.”

Namrata Shivshankar patil

Screened

Mid-level Backend/Full-Stack Engineer specializing in cloud, AI, and distributed systems

Arlington, TX6y exp

“Built and shipped internal AI support systems spanning Angular/TypeScript frontends, Java/Spring/AWS backends, and Claude-powered troubleshooting workflows. Stands out for combining full-stack product delivery with practical LLM engineering, including RAG, structured outputs, production evals, and careful human-in-the-loop safety decisions. Has shipped systems serving 150-800 daily sessions at 99.5% availability while reducing repetitive support burden.”

“Backend-leaning full-stack engineer who has shipped real-time, customer-facing dashboards and ticketing/payment features at Freshworks and Global Payments. Strong in Python API design (Django/Flask/FastAPI) and React/TypeScript UIs, with hands-on experience scaling PostgreSQL for high transaction volumes and operating services on AWS, including incident response and HIPAA-aligned security controls.”

Saumay Killa

Screened

Mid-level Full-Stack Engineer specializing in AI SaaS and web applications

New York, NY3y exp

“Built a career platform feature end-to-end that generates tailored resumes and cover letters using a React/TypeScript frontend, Postgres, and AWS Lambda/SQS backend. Strong in event-driven, serverless architecture and pragmatic product iteration, with a quantified 60% improvement in onboarding completion after redesigning the UX with resume parsing and a multi-step flow.”

“Backend-leaning full-stack engineer with 7 years of Python experience who has worked on data-heavy products in both healthcare and social media intelligence. Particularly notable for driving Elasticsearch-based search improvements on a B2B social media analytics platform and for building secure healthcare APIs using Flask/Django with OAuth, JWT, and multiple databases.”

“Backend/full-stack engineer at Qualcomm who built and operated a drift monitoring platform for 10k+ edge AI models. Stands out for combining strong TypeScript/React/Node execution with production-grade systems thinking across PostgreSQL tuning, Redis caching, ECS deployments, and Kafka-based architectural improvements that measurably improved reliability and release speed.”

“Backend engineer with experience evolving a high-volume agricultural loan processing platform (APMS) at HDFC Bank, emphasizing transactional integrity, auditability, and modularity while integrating with credit bureaus, document management, and risk engines. Also improved automation/reporting robustness at Trend Micro by catching duplicate-event retry edge cases and adding idempotency safeguards.”

“At KPMG, built and productionized a secure RAG-based LLM assistant that lets business and risk stakeholders query data warehouses in natural language, reducing dependence on data engineers for ad-hoc analysis. Demonstrates strong production rigor (Airflow orchestration, CI/CD, containerization), retrieval/embedding tuning (rechunking, semantic abstraction for structured data), and reliability controls (confidence thresholds, refusal behavior, monitoring and canary evals).”

“Open-source JavaScript contributor focused on performance and maintainability in data visualization libraries—refactored legacy ES5 into modular ES6, added tests/docs, and delivered ~30% faster load times with positive community adoption. Also optimized a React dashboard (~40% load-time reduction) and took ownership in an ambiguous AI product initiative by setting milestones, standing up an initial ML pipeline, and shipping a prototype in ~6 weeks that became the basis for production.”