Vetted FastAPI Professionals

Jonathan McGrath

Screened ReferencesModerate rec.

Senior Full-Stack/AI Engineer specializing in mobile and web product development

San Francisco, CA14y exp

“Built an end-to-end mobile + web Q&A marketplace connecting users with professionals, including real-time chat and Stripe-based monetization (products/subscriptions). Hands-on with scaling Firebase/Firestore (subcollections, composite indexing, pagination) and mobile caching/sync challenges. Also created an internal AI-driven report generator that turned chatbot outputs into curated graphs and PDFs for marketing, iterating based on stakeholder feedback.”

“Data engineer in financial services (Northern Trust) who has worked across ingestion, transformation, data quality, orchestration, and serving on AWS (S3/Glue/EMR) with Airflow. Highlights include processing ~15M transactions with validation/anomaly detection for regulatory reporting and improving Snowflake query performance by 27% for risk/compliance reporting. Also built a personal real-time streaming service (FastAPI, Kafka, Redis, Cassandra) and uses production reliability patterns like blue-green/atomic swaps and robust retry strategies.”

Dhananjay Dubey

Screened

Senior Full-Stack Game Engineer specializing in multiplayer Unity and mobile systems

Newark, NJ6y exp

“Unity/C# game developer with hands-on experience shipping large-scale multiplayer mobile games, including titles cited at 1M+ and 10M+ downloads. Combines real-time networking and physics optimization expertise with AI/MR research experience, including an IEEE-published sports coaching system using pose estimation, SMPL-X, and LSTM models. Particularly strong in latency-sensitive, cross-platform interactive systems spanning mobile, multiplayer, and mixed reality.”

Rangel Koli

Screened

Entry-level Software Engineer specializing in backend and cloud systems

New York City, NY1y exp

“Backend engineer who built and scaled a zero-to-one social product backend using Supabase (Postgres, Edge Functions, Auth, Realtime) plus Neo4j for graph-based friend recommendations. Demonstrates strong production rigor: staged rollouts with metrics, incident rollback/postmortems, and complex schema refactors using expand-contract/dual-write with reconciliation and feature flags. Notably proactive about edge cases like geo-boundary realtime delivery and idempotent retry safety.”

Drashti Magia

Screened

Junior AI Engineer specializing in LLMs, multimodal ML, and applied machine learning

San Jose, CA3y exp

“Software engineer with a disciplined, production-minded approach to AI-driven development: uses ChatGPT, Claude, GitHub Copilot, and scoped coding agents to accelerate delivery without giving up architectural judgment. Notably applied a multi-agent workflow on ClinicOps Copilot, using agents for planning, Bedrock/RAG scaffolding, and failure testing while personally owning architecture, grounding quality, and end-to-end review.”

“AI-focused engineer with Infosys experience building Azure/.NET chatbot applications and recent hands-on work with FastAPI/LangChain. Built a hackathon multi-agent legal counsel system showcasing agent orchestration, and emphasizes production readiness via Docker, GitHub Actions CI/CD, pytest automation, and adversarial simulations for auditable AI behavior. No direct robotics/ROS experience to date.”

“LLM/agent engineer who has built production multi-agent systems (LangChain/LangGraph) for enterprise workflows like email and calendar automation, with a strong focus on latency, tool-calling accuracy, and evaluation via LangSmith. Also worked on AI long-term memory using knowledge graphs at VEAI and communicated the approach and tradeoffs to CEO/CTO stakeholders.”

sai anuragh Sangoju

Screened

Mid-level AI/ML Engineer specializing in fraud detection, credit risk, and NLP

Dallas, Texas4y exp

“Built and deployed a production LLM-powered university support chatbot on Azure using a RAG pipeline, focusing on reducing hallucinations, improving latency, and handling ambiguous queries via confidence checks and clarification prompts. Also has hands-on orchestration experience (Airflow/Azure Data Factory), including hardening a demand-forecasting ingestion workflow with sensors, retries, and automated alerts, and uses a metrics-driven testing/monitoring approach for reliable AI agents.”

Mark Wlodawski

Screened

Senior Unity Developer specializing in AI/LLM systems and multiplayer VR

Orlando, FL11y exp

“Backend/data engineer focused on AWS-native Python systems: built a FastAPI microservice on ECS/Fargate serving real-time analytics at millions of daily requests with strong reliability (OAuth2/JWT, retries/timeouts, correlation IDs) and autoscaling. Also delivered Glue/PySpark ETL pipelines to curated S3 Parquet/Athena with schema evolution + data quality controls, owned Airflow pipeline incidents, and has a track record of measurable performance and cost optimizations (e.g., ~80%+ query latency reduction; reduced logging/NAT/Fargate spend).”

“Built a production RAG chatbot for Worcester Polytechnic Institute that indexes 500+ webpages using FAISS + Llama 3, with strong grounding/hallucination controls (confidence thresholds and citations). Also has internship experience orchestrating multi-step ETL pipelines with AWS Step Functions and delivered a 30x faster fraud/claims triage workflow at Munich Re using association rules and stakeholder-friendly dashboards.”

“Built and deployed a production LLM-powered RAG knowledge system to unify operational/policy information across PDFs, wikis, and databases, emphasizing auditability and low-latency/cost performance. Improved answer relevance at scale by moving from pure vector search to hybrid retrieval with metadata filtering and reranking, and partnered closely with healthcare operations/compliance to define acceptance criteria and human-in-the-loop guardrails.”

“New grad focused on AI systems and agent-based development, with hands-on experience using LLMs as a coding partner and building RAG-based document processing workflows. Stands out for practical experimentation with semantic chunking, retrieval optimization, and multi-agent architectures, including redesigning a RAG workflow by adding a reasoning agent to improve response accuracy and reliability.”