Vetted FastAPI Professionals

“AI/ML engineer who shipped a production blood-test report understanding and personalized supplement recommendation product, using a LangGraph multi-agent pipeline on AWS serverless with OCR via Bedrock and RAG over vetted clinical research. Also built end-to-end recommender system pipelines at ASANTe using Airflow (ingestion, embeddings/features, training, registry, batch scoring/monitoring) with KPI reporting to Tableau, with a strong focus on safety, evaluation, and measurable reliability.”

“Backend/ML engineer who has built both enterprise data pipelines and real-time AI products: modular Python (Flask/FastAPI) services integrating automation scripts and low-latency ML inference (MediaPipe, PyTorch) plus OpenAI-powered feedback. Demonstrated measurable performance wins (~30% faster HR workflows; ~40% faster AWS pipelines across 100+ Oscar Health feeds) and strong multi-tenant/data-isolation patterns (schema-based isolation, RBAC, microservices).”

“Backend engineer who built an agentic LLM system for private equity/finance that answers questions over enterprise contracts and documents using a vector-db RAG pipeline. Differentiator is a trust-focused citation framework (with highlighted source text) to reduce hallucinations in high-stakes workflows, plus strong DevOps experience deploying microservices on Kubernetes with Helm/GitOps and building Kafka real-time pipelines.”

“Backend/data engineer with hands-on production experience building Python (FastAPI/Flask) data enrichment services secured with Okta OAuth2 and monitored via Splunk/Dynatrace. Has delivered AWS event-driven and data-migration solutions (Lambda + Kafka to EKS; Glue from on-prem Oracle to S3/data lake) and modernized Informatica match/merge logic to cloud services using parallel-run parity validation and stakeholder sign-off.”

“Data science/AI intern at University at Buffalo Business Services who built and deployed production systems spanning classic ML and LLM assistants. Delivered real-time competitor intelligence for a Cornell-partnered, $1B beverage launch by scraping/cleaning 5,000+ SKUs and deploying models via API, then built a domain-aware LLM assistant to modernize Excel-based workflows with strong grounding, privacy controls, and sub-5s latency.”

“Backend engineer who built and evolved a startup data-processing backend (Express.js/MySQL) handling millions of user data points, with a microservices pipeline integrating multiple social media APIs. Emphasizes reliability and security through comprehensive testing, robust error/retry handling for sequential pagination constraints, and tight IAM/JWT/OAuth-based access controls.”

“ML engineer with hands-on experience taking a Gaussian Process Regression-based intelligent survey timing system from build to real-world deployment, including a 3-week RCT on 120 participants and measurable improvements (15% response rate, 23% data quality). Also served as a key technical resource at CData for customer-facing demos and debugging hundreds of production issues, bridging engineering with Sales and Customer Success.”

“Software engineer with hands-on experience building data-intensive and 3D-processing web applications (React/Next.js/TypeScript + Node.js). Has worked in microservices using RabbitMQ for event-driven workflows and built an internal ops/engineering dashboard to monitor pipeline jobs, surface logs, and manage retries—improving visibility and reducing on-call/debug time.”

“Software engineering intern experience at Zoho Corp and Zeus Desk building and deploying customer-facing systems. Delivered a real-time booking platform backend that stayed stable for 1,000+ users by optimizing MySQL queries/indexing and shipping hotfixes during production latency incidents. Also integrated financial operations APIs across 50+ small-bank partners by creating a normalization/validation layer to handle inconsistent partner data and prevent integration breakages.”

“Robotics software engineer with deep ROS2 experience who owned the perception stack for an automated C. elegans manipulation system—building YOLO-based worm segmentation plus OCR label reading and integrating it into a MoveIt2 pipeline with real-time latency constraints. Also deploying ROS2 on an AgileX Tracer with ZED depth camera for vision-based person following and working on SLAM/sensor fusion, with additional production-style ML deployment experience (Dockerized FastAPI + PyTorch on AWS EC2 with CI/CD).”

“AI/LLM engineer who has shipped production systems end-to-end, including a note-taking product (Notey) combining audio/image capture, ASR, summarization, and a semantic chat agent over past notes. Also has applied ML experience in healthcare, collaborating directly with doctors to validate an EEG seizure-detection pipeline, and uses Kubernetes to optimize GPU usage for LLM training.”

“Student building a production trip-planning LLM agent (LangChain + Streamlit) that routes user queries across multiple tools (maps/places/Wikipedia). Implemented zero-shot multi-label intent detection with priority rules to handle multi-intent requests, and collaborates with a startup product manager to shape tone, features, and user experience.”

“AI automation builder who shipped an OpenAI-powered weekly "trending AI tools" WoW reporting system (65 categories) that reduced a 6–7 hour manual process to ~10 minutes at negligible API cost. Also building a RAG-based content creation prompt engine that turns PDFs into storyboards with fact-checking/traceback to source lines, plus experience with AWS deployment components (Lambda, ECR, App Runner, Bedrock, API Gateway) and GitHub Actions.”

“Hands-on LLM/RAG engineer with production experience improving retrieval quality and stability by addressing messy data, vector DB inaccuracy, and top-K issues—ultimately redesigning to hybrid search with tuned keyword/semantic weighting and MCP-based data supplementation. Also brings strong AKS/Kubernetes deployment experience, optimizing CI/CD speed via lightweight local Docker validation and decomposing pods to avoid full rebuilds, plus a metrics-driven approach to agent/workflow testing and traceability.”

“Backend-focused Python engineer who owned high-traffic internal services end-to-end (FastAPI/Django) including REST/GraphQL APIs, PostgreSQL optimization, async task processing via SQS, and full CI/CD. Strong Kubernetes-on-EKS and GitOps (ArgoCD + Helm) experience, plus Kafka real-time streaming work and phased cloud-to-on-prem migration support.”

“Full-stack engineer who builds and scales customer-facing and internal AI products end-to-end (React/TypeScript/FastAPI/MongoDB) with strong product instrumentation and rapid MVP iteration. Built an AI-powered code review assistant adopted across teams and integrated into CI/CD, reducing manual review time by 30%+, and has hands-on experience with LLM retrieval/reasoning systems (LangChain + FAISS) and microservices scaling using RabbitMQ, Docker, and AWS.”

“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.”

“Accenture engineer with hands-on experience taking an NLP sentiment analysis system from prototype to production, emphasizing robustness to noisy data, scalability, and observability (dashboards for latency/error/throughput). Also supports customer-facing teams with demos and PoCs, translating client requirements into secure, scalable architectures and troubleshooting LLM/agent workflows via logs and step-level traces.”

“Backend/embedded-focused engineer with hands-on experience designing real-time, memory/power-constrained firmware architectures and also building Python/FastAPI services. Demonstrates strong production rigor across migrations (strangler pattern, shadow reads, feature flags), security (OIDC/JWT, RBAC/ABAC, Postgres RLS), and robustness for async workflows (idempotency, event ordering, state machines).”