Vetted FastAPI Professionals

“Full-stack engineer with hands-on ownership of a customer-facing advanced performance metrics experience in the Amazon S3 console, spanning React UI, Python/Node services, Redshift/RDS data access, and AWS IaC/CI-CD with CloudWatch/Route53 operational readiness. Demonstrates strong production instincts around resilience (partial failures, multi-region inconsistencies), progressive rollouts/feature flags, and reliable ETL/integration patterns (idempotency, backfills, reconciliation).”

“Backend engineer with hands-on experience building Flask REST APIs (auth, CRUD, S3 media uploads) and driving measurable Postgres/SQLAlchemy performance gains (p95 reduced to 200–400ms by eliminating N+1s and switching to keyset pagination). Implemented multi-tenant isolation with strict tenant scoping plus Postgres RLS, and built an OpenAI-powered quiz generation pipeline using queued workers, structured JSON outputs, and Celery/Redis optimizations to stabilize high-throughput workloads.”

“Backend engineer focused on scalable, security-first platform architecture—recently built an end-to-end centralized access-control system that launched successfully with ~50k early adopters and was designed to support ~10x traffic growth. Experienced in production authn/authz (token verification, handoff/session migration), and in de-risking migrations via feature flags, phased rollouts, A/B testing, and Splunk-based monitoring.”

“Backend engineer with experience at MetLife and Amazon focused on security and control for internal and customer-facing services. Emphasizes contract-first Python/FastAPI APIs with strong auth (JWT + RBAC/claims), data-layer isolation (RLS/tenant scoping), and reliability practices like incremental refactors, rollback planning, and idempotency to handle retry-driven failure modes.”

“Built and deployed a production multi-agent RAG system at JPMorgan Chase to automate regulated credit analysis and compliance clause discovery across large internal policy/document libraries. Implemented LangGraph-based supervisor orchestration with structured state management (Azure OpenAI) to support long-running, resumable workflows, plus hybrid retrieval + re-ranking and guardrails for reliability. Strong at evaluation/observability (trace logging, LLM-judge, HITL) and at communicating results to non-technical stakeholders via Power BI embeds and Streamlit prototypes.”

“Full-stack engineer with experience at Apple, Walmart, and a healthcare startup (Legacy), building customer-facing PWAs and internal platforms. Delivered HIPAA-compliant clinician/patient workflows with rapid weekly releases and measurable engagement gains, and built scalable automation/testing and real-time analytics systems using Next.js/React/TypeScript, Node/FastAPI, Redis, and PostgreSQL.”

“Built and shipped a production LLM-powered multi-agent RAG system to automate complex internal support workflows, integrating tool execution (SQL/APIs) with validation guardrails to reduce hallucinations. Optimized for real-world latency and cost via model routing, caching, and async parallel tool calls, and enforced reliability with CI-gated golden test sets derived from anonymized production queries.”

“Built a production LLM-powered RAG agent for healthcare/insurance operations that retrieves and summarizes patient medical documents with grounded citations, scaling to ~4.5M records. Addressed medical shorthand and terminology by fine-tuning ~120 lightweight DistilBERT models by specialty and validating entities against SNOMED/RxNorm, while using SHAP/LIME and human-in-the-loop review to make decisions explainable to stakeholders.”

“Backend/LLM engineer with experience productionizing RAG systems (legal-case natural language querying) and optimizing for latency/cost, including a reported ~40% reduction via Redis caching and batching. Built monitoring and real-time debugging workflows (FastAPI, structured logging, correlation IDs, sandbox repro) and regularly delivered technical demos/workshops. Also partners with BD/sales to translate LLM capabilities into business value, including ESG-metric extraction from corporate filings.”

“Research assistant at Syracuse University who owned a Python/FastAPI analytics backend for user-uploaded large datasets, using S3 streaming uploads and background workers for heavy processing. Has hands-on experience deploying Dockerized Python/Java microservices to AWS EKS with Jenkins-based CI/CD, plus Kafka-based event-driven pipelines and practical migration patterns (dependency mapping, dual-write, reconciliation) to minimize downtime.”

“AI engineer with production experience building a high-accuracy (98%) fraud detection system operating at real-time latency (1–2s) over millions of transactions, using a multi-model pipeline approach to meet performance constraints. Also implemented Airflow-orchestrated workflows (DAGs, retries, alerts) to replace brittle cron scripts and is currently pursuing a master’s project on real-time ASL-to-text conversion.”

“Backend-leaning full-stack engineer with e-commerce and analytics experience who modernized synchronous order workflows into a Kafka-based event-driven architecture (Java/Spring Boot) to reduce checkout latency and peak-traffic failures. Has built production FastAPI services with JWT/RBAC and strong testing/observability, delivered React+TypeScript reporting dashboards, and handled AWS scaling incidents end-to-end (RDS read latency mitigated with read replicas and query tuning).”

“Full-stack engineer with Cisco supply-chain and Wipro internal platform experience, focused on customer-facing UI performance and secure backend services. Built a bulk Excel inventory upload feature (Spring Boot/Apache POI) that cut manual effort ~80%, and delivered high-scale Angular/React dashboards with strong reliability/observability (FastAPI, JWT, Docker, AWS, AppDynamics).”

“Product-focused full-stack engineer building a GenAI-powered case summarization workflow for a telemetry dashboard, spanning React/TypeScript UI (confidence indicators, reasoning traces) and Python/FastAPI backend with caching to control LLM latency/cost. Has operated services on AWS (ECS Fargate, RDS Postgres, S3) and Kubernetes, and has hands-on experience resolving real production latency incidents through query/index optimization and caching.”

“Full-stack engineer building real-time internal banking operations dashboards (Java/Spring Boot microservices + React/TypeScript) with Kafka-based streaming and post-launch performance optimizations. Also shipped a production internal AI support assistant using RAG (Confluence/PDF/support docs ingestion, embeddings + vector DB retrieval) with guardrails, evaluation loops, and observability to reduce hallucinations and prevent regressions.”

“TypeScript backend engineer in the robotics domain with hands-on experience building low-latency (20–40ms) production systems using RabbitMQ, Redis, and HA PostgreSQL (Patroni). Has owned end-to-end services supporting 15 clients via config-driven architecture, with strong CI/CD, automated testing, and observability (OpenTelemetry) practices, plus API versioning/deprecation using Keycloak auth.”

“Software engineer (4+ years) with hands-on production GenAI experience: built an AI incident triage assistant that summarizes production logs for on-call engineers and iterated it using real incident metrics (time-to-signal, triage duration). Also shipped a RAG-based customer support knowledge assistant using embeddings + vector retrieval with strong guardrails (relevance thresholds/abstain, sanitization, auditing) and a formal eval loop (500-query gold set) that drove measurable retrieval improvements.”

“Product-minded software engineer with experience shipping AI-powered financial insights (spend forecasting, cashflow, credit optimization) and building real-time analytics systems using React/TypeScript and FastAPI. Has designed microservices with RabbitMQ/gRPC and strong observability (Prometheus/Grafana/OpenTelemetry), and also built an internal Figma plugin adopted by designers that reduced export time by 50%.”

“Data/ML backend engineer with hands-on production experience spanning RAG services (LlamaIndex/OpenAI) and AWS data platforms. Has delivered Terraform-managed AWS architectures (Lambda + ECS Fargate) with secure secrets handling, built Glue-to-Redshift ETL with schema evolution controls, modernized SAS reporting into Python microservices, and achieved major Redshift query speedups (2+ hours to under 15 minutes).”

“Built an internal Meta tool (HiVA Bot) for notification customization and end-to-end task tracking around advertiser-reported issues, including chat-thread creation, org-hierarchy opt-ins, SLA reminders, and search/typeahead features. Implemented the system with a Java/Spring Boot microservices approach and asynchronous patterns, and supported adoption via internal wiki documentation.”

“Software engineer focused on reliability and scalable systems: built React/TypeScript dashboards backed by Java/Spring Boot APIs and designed Kafka-based microservices with strong contract/versioning discipline. Known for shipping incremental improvements with tight feedback loops and for creating internal observability tools that streamline on-call and incident diagnosis under high-traffic conditions.”

“AI/ML practitioner who built an Indian Sign Language recognition system (MediaPipe hand keypoints + CNN/RNN) as an accessibility-focused teaching aid, iterating closely with advocacy groups and educators and reaching 92% accuracy. Also has production-scale data migration experience at Saasgenie, using Kubernetes pod parallelization to migrate 1M+ ITSM records with a 5x throughput gain under API rate limits.”

“ML/LLM engineer who built production systems to speed up artist content-creation workflows, including a fine-tuned image captioning model paired with a RAG layer over image embeddings/captions to improve consistency across changing domains. Experienced orchestrating multi-tool agents with LangChain/LangGraph (planning + critic/reflection) and setting up practical monitoring (caption rejection rate) plus evaluation sets for tool-calling accuracy, output quality, and latency.”