Vetted FastAPI Professionals

“Backend-focused developer who built LinguaTile (language learning app) on a FastAPI + MongoDB monolith deployed to Google Cloud Run, emphasizing async performance and security (RBAC/JWT, rate limiting, request tracing). Also created Mark-RS, a static HTML generator with a 100% CommonMark-compliant Markdown parser, demonstrating strong edge-case rigor and systems robustness.”

Kumar Manik

Screened

Intern AI Engineer specializing in LLMs, MLOps, and RAG systems

0y exp

“Built and shipped a production-grade RAG-powered news summarization and Q&A product, tackling real-world issues like retrieval drift, hallucinations, latency, and autoscaling deployment (Docker + FastAPI + Streamlit Cloud). Experienced in end-to-end ML/LLM workflow automation using Airflow, Kubeflow Pipelines, and MLflow, and has demonstrated business impact (40% inference precision improvement) through close collaboration with non-technical stakeholders at Evoastra Ventures.”

Rutvi Rathod

Screened

Junior Full-Stack Software Engineer specializing in AI and web applications

San Jose, CA1y exp

“LLM/AI backend engineer with hands-on experience taking customer LLM prototypes into production using FastAPI, containerization, CI/CD, and OpenTelemetry-based observability. Demonstrated measurable impact by cutting LLM costs ~40% and reducing workflow errors ~50% through schema-enforced outputs, better tool definitions, retries, and prompt/model optimization; also supports pre-sales via technical discovery and rapid integration demos.”

Anjali Chepuri

Screened

“Built and deployed an LLM-powered financial document processing and summarization platform at Morgan Stanley using a production RAG pipeline (PDF ingestion, embedding-based retrieval, schema-constrained JSON outputs) delivered via FastAPI microservices on Kubernetes. Drove measurable impact (40% reduction in manual review time) and improved factual accuracy for numeric fields by 30% through metadata-aware retrieval, strict schemas, and post-generation validation, with a human feedback loop from financial analysts.”

Madireddy Sai Prasad Reddy

Screened

“Full-stack engineer who has owned workflow-builder/editor products end-to-end, including multi-tenant graph validation platforms built with TypeScript/React and Node. Experienced designing microservices with RabbitMQ/SQS-style queues (idempotency, retries, DLQs) and building internal admin CMS tools (SvelteKit + FastAPI) that replaced manual spreadsheet processes and became the operational source of truth.”