Vetted FastAPI Professionals

“Full-stack + ML engineer who built “Finsight,” a real-time financial risk platform (React/FastAPI/MongoDB/AWS Lambda) processing 2M+ records monthly, using sharding and Redis caching (60% DB load reduction) plus async and batch optimizations. Also has healthcare product experience at Apollo Healthcare, partnering directly with clinicians/admins to design and iterate EHR dashboards via Figma prototyping and user testing, and demonstrates clear system design thinking for real-time voice-to-LLM architectures.”

“Backend engineer with experience in fraud reporting and billing systems, building Java/Spring Boot services behind a React frontend and improving performance 40%+ with caching and SQL optimization while maintaining 99.9% uptime. Has hands-on experience migrating a monolith to microservices with incremental rollout, clear data ownership boundaries, and production-grade API reliability/security practices (JWT/OAuth, RBAC, row-level scoping).”

“Backend engineer who built and evolved the real-time networking/messaging backend for a cross-platform professional networking app (Make Connexions), optimizing for low-latency delivery, privacy, and strong consistency. Experienced scaling Python/FastAPI APIs with async + Redis, and leading safe refactors via versioned endpoints, feature flags, and backward-compatible migrations; strong production auth/RLS expertise including refresh-token rotation edge cases.”

“Backend/data engineer who reports building production Python services (FastAPI + JWT) backed by Postgres and Redis, and modernizing data workflows using AWS Glue + PySpark with S3/RDS. States experience delivering AWS solutions (S3, SES, Cognito) and using golden datasets/snapshot testing for migration parity, with many details withheld due to NDAs. Seeking fully remote work with a $300k base salary expectation.”

“Backend-focused Python engineer who has owned production FastAPI services deployed on Kubernetes, including CI/CD (GitLab CI to ECR) and GitOps delivery via ArgoCD/Helm. Has hands-on experience with complex reliability and infrastructure work—solving data inconsistency with validation/partial-data paths, fixing K8s liveness issues via lazy loading, and supporting a phased cloud-to-on-prem migration with dual-writes and monitoring. Also built Kafka-based real-time ingestion consumers handling bursty, high-throughput traffic with async processing and topic/retention tuning.”

“Built and deployed an LLM-powered mental health therapy assistant at AppHealth that segments users by stress level and delivers personalized, non-medical guidance. Implemented healthcare-focused safety guardrails (secondary LLM output filtering) and a multi-agent router workflow validated via statistical tests and therapist review, then scaled training/inference on AWS (EC2/Lambda/DynamoDB) with Kubernetes.”

“Backend/data engineer focused on production-grade Python services and AWS platforms: builds FastAPI microservices on EKS with strong reliability patterns, CI/CD, and observability. Also delivers AWS Glue/Redshift analytics pipelines with schema-evolution and data-quality safeguards, and has modernized legacy batch processing into maintainable services with parallel-run parity validation and feature-flagged rollouts.”

“Backend engineer with experience in both healthcare (Siemens) and payments (Bitwise), focused on scaling Python APIs and modernizing architectures. Has led monolith-to-microservices migrations and introduced Kafka async processing, Redis caching, and ELK observability, citing ~40% faster issue resolution and improved reliability via idempotency and strong security controls (OAuth2/JWT, RBAC, RLS).”

“Built and deployed a production LLM-powered support assistant for customer support agents using a RAG architecture over internal docs and past tickets, with human-in-the-loop review. Demonstrates strong applied LLM engineering focused on real-world constraints (hallucinations, latency, cost) using routing to smaller models, reranking, caching, and rigorous evaluation/monitoring (offline eval sets, A/B tests, KPI tracking).”

“Software engineer (Yellow.ai) who built and productionized an AI-driven resume tailoring system using embeddings + Chroma RAG + QLoRA fine-tuning, deployed via Docker/Kubernetes with CI/CD on a CPU-only Oracle VM. Demonstrates strong reliability/evaluation rigor (custom hallucination/coverage/relevance metrics) and measurable business impact, including a 60% user satisfaction lift from improving chatbot intent accuracy with product and support teams.”

“Built and deployed a production enterprise LLM-powered RAG assistant for the construction domain, enabling natural-language querying across PDFs/reports and structured sources (SQL/CSV). Implemented an agent-based routing and multi-agent orchestration approach (LangChain/LangGraph) to reduce hallucinations, improve latency, and deliver actionable, structured responses based on stakeholder feedback.”

“Internship project at Builder Market: built an end-to-end production multimodal LLM application that estimates renovation/replacement costs from appliance photos (CLIP embeddings) or text descriptions, combining fine-tuning with agentic RAG. Focused heavily on real-world performance constraints—latency and cost—using parallel agent workflows, model routing to smaller/open-source models, re-ranking, and retrieval chunking, and collaborated closely with CEO/co-founders to deliver the solution.”

“Built and deployed a real-time hospital readmission risk prediction system at NYU Langone Health, combining structured EHR data with BERT-based NLP on clinical notes and serving predictions to clinicians via Azure ML and FHIR APIs. Emphasizes production reliability and clinical trust through SHAP-based explainability and robust healthcare data preprocessing, and reports a 22% reduction in 30-day readmissions.”

“Customer-facing software engineer who rapidly turns business requirements into Figma prototypes and PoC applications, using workflow prioritization and frequent client reviews to stay aligned. Has hands-on experience integrating with existing authentication/user APIs, building MongoDB-backed caching, and implementing robust fallback/retry mechanisms. Comfortable working on-site with customers and resolving production issues in AWS (e.g., DNS/EC2 traffic routing) in collaboration with DevOps.”

“Built and deployed a production RAG system for financial/compliance teams using GPT-4, Claude, and local models to retrieve and summarize thousands of internal documents with strong security controls (role-based retrieval, PII masking). Drove significant operational gains (30+ hours/week saved, ~35% productivity lift, ~45% faster responses) and orchestrated end-to-end ingestion/embedding/index refresh pipelines with Airflow, S3, and SageMaker while partnering closely with compliance stakeholders on auditability and traceability.”

“Robotics-focused full-stack engineer with hands-on ROS experience building sensor-processing and control nodes, plus a track record of debugging and optimizing real-time robot responsiveness via profiling and message-timing analysis. Uses Webots for pre-hardware validation and Docker/CI/CD to standardize deployments and catch issues early.”

“ML/LLM engineer with production experience building a RAG-based LLM support assistant (FastAPI, Redis, Kafka) with multi-layer validation and human-in-the-loop feedback loops to improve accuracy over time. Has orchestration and MLOps depth using Airflow and Kubeflow on Kubernetes (autoscaling, alerting, monitoring) and delivered measurable ops impact (40% ticket efficiency improvement) by partnering closely with customer support teams.”

“Backend/ML platform engineer who owns end-to-end delivery of ML-serving APIs (FastAPI + TensorFlow) and runs them reliably on Kubernetes using ArgoCD GitOps. Has hands-on experience solving production-only issues (probe tuning for model warm-up, resource profiling) and building scalable Kafka streaming pipelines, plus supporting phased on-prem to AWS migrations with dependency discovery and recreation of hidden jobs/workflows.”

“Built and deployed an AI agent to help patients navigate complex housing information by scraping and normalizing unstructured data across all 50 U.S. states, then layering a LangChain RAG system with MMR re-ranking to reduce hallucinations. Experienced in orchestrating multi-agent workflows (LangGraph/CrewAI) and production reliability practices (Pydantic-validated outputs, LLM-as-judge evals, tracing). Also delivered stakeholder-facing explainability via SHAP dashboards for a loan-approval predictive model at Welspot.”

“ML engineer/data scientist who deployed a production credit risk + insurance claims triage platform at Hartford Financial, combining XGBoost default prediction with BERT-based document classification. Demonstrated strong MLOps by cutting inference latency to sub-500ms and building drift monitoring plus automated retraining/deployment pipelines (MLflow, CloudWatch, GitHub Actions, SageMaker) with human-in-the-loop review and SHAP-based explainability for underwriting adoption.”

“NLP/ML engineer with hands-on healthcare and support-ticket text experience, building clinical-note structuring and semantic linking systems using spaCy, BERT clinical embeddings, and FAISS. Emphasizes production-grade delivery (Airflow/Databricks, PySpark, Docker, AWS/FastAPI/Lambda) and rigorous validation via clinician-labeled datasets, retrieval metrics, and user feedback.”

“Robotics/perception engineer focused on production-grade, real-time systems—optimized self-supervised segmentation on Jetson Nano from ~6–10 FPS to ~20–25 FPS and scaled experimentation/deployment by unifying 15+ edge models in a modular PyTorch Lightning framework. Experienced integrating distributed LiDAR-camera fusion via gRPC/protobuf into mission planning, migrating ROS1→ROS2 Foxy for multi-drone perception, and adding Prometheus-based observability for long-running deployments.”

“Built and deployed NeuroDoc, a production-grade RAG system for PDF Q&A that delivers citation-backed answers with strong anti-hallucination guardrails. Experienced in orchestrating and scaling ML/LLM pipelines with Kubernetes, Airflow/Prefect, and PyTorch Distributed, and in building rigorous evaluation and citation-verification tooling to ensure reliability in production.”

“Built and shipped an AI-powered driver ranking/assignment system at AffirmoAI using LLM intent classification + RAG over pgvector/Postgres, served via FastAPI with a React UI that explains scores. Drove measurable improvements through optimization and iteration (latency down to <800ms, adoption 60%→90%+) and implemented rigorous eval loops with dispatcher ground truth plus cold-start handling for new drivers.”