Vetted FastAPI Professionals

“Backend-leaning full-stack engineer with logistics domain experience (DHL) who shipped a real-time shipment status update system using Spring Boot + Kafka and a performance-tuned PostgreSQL tracking schema. Also has AWS production operations experience (ECS/Kubernetes, Jenkins CI/CD, Terraform/Ansible) and has handled peak-load incidents end-to-end by tracing Kafka lag to database bottlenecks and resolving via query/index optimization plus scaling.”

“Full-stack/backend engineer with deep insurance claims domain experience who built and operated a microservices + ETL platform (Java/Spring Boot + Python + Kafka/Databricks) processing 1M+ daily transactions. Combines production-grade reliability (99.7% uptime, zero-downtime blue/green releases, strong observability) with customer-facing UI delivery (AngularJS/React+TS dashboards and a hackathon-winning research chatbot).”

“Full-stack engineer with healthcare domain experience (UnitedHealthcare) delivering real-time claims/eligibility dashboards using Spring Boot microservices and React/TypeScript, with strong AWS/Kubernetes DevOps. Demonstrated measurable impact through performance tuning (33% faster retrieval; 45% faster responses during a 60% traffic spike) and HIPAA-aligned security practices. Also built production FastAPI services for high-volume financial transactions with strong testing and observability (95%+ coverage; Prometheus/Grafana).”

“AI/ML-focused backend engineer who shipped two production systems: PersonaPal (agentic LLM chatbot with RAG, FAISS-based retrieval, and Redis semantic caching) and CervixScan (clinical diagnostics platform with PostgreSQL data modeling and human-in-the-loop safety for low-confidence predictions). Demonstrates strong performance/reliability work (indexed vector search, caching, query optimization to ~200ms) and end-to-end ownership from orchestration design through deployment.”

“Built a production, real-time clinical documentation system at HCA that converts doctor–patient conversations into structured clinical summaries using speech-to-text, LLM summarization, and RAG. Demonstrated measurable gains from medical-domain fine-tuning (clinical concept recall +18%, ROUGE-L 0.62 to 0.74) while meeting HIPAA constraints via PHI anonymization and encryption, and deployed via Docker/FastAPI with CI/CD and monitoring.”

“Data engineer with Webster Bank experience owning end-to-end pipelines (APIs + databases) processing millions of records/day, improving data quality (25–30% fewer issues) and reliability (~99.9% successful runs). Built resilient external data ingestion/scraping systems (schema-change validation, idempotent backfills, monitoring/alerts) and shipped a FastAPI service exposing curated datasets with versioning and consistently low latency.”

“Backend engineer with experience at KPMG evolving an audit/reporting platform from monolithic components to microservices (Spring Boot/Node.js), improving API performance and enabling independent deployments. Demonstrates strong production focus across secure API design (FastAPI, JWT/OAuth2, RBAC/RLS), incremental migrations with feature flags, and robustness improvements like optimistic locking to prevent race conditions.”

“Backend engineer who built the core backend for Air Kitchens’ discovery/booking platform on AWS (Node + Python, DynamoDB, SQS/Lambda), optimizing for fast user-facing APIs and scalable async workflows. Introduced an AI matching service with a deterministic pre-filter + LLM ranking approach to balance latency vs quality, and has hands-on experience with production security (JWT/RBAC/RLS), CI/CD, and blue-green, staged migrations from Django to modular services.”

“Backend engineer with experience at Cigna evolving REST API services backed by PostgreSQL, emphasizing reliability/correctness, scalability, and observability. Has hands-on production experience with FastAPI (contract-first design, Pydantic schemas), performance tuning (indexes, caching), and secure auth patterns (OAuth/JWT, RBAC, row-level security via Supabase), plus low-risk incremental rollouts using feature flags and dual writes.”

“Backend/data engineer from ALDI Tech Hub who modernized legacy analytics (Excel/SAS) into production-grade Python services on AWS serverless (FastAPI on Lambda behind API Gateway with Step Functions). Strong in reliability and operations (Cognito auth, retries/timeouts, structured logging, CloudWatch alarms) and data pipelines (Glue ETL with schema evolution); delivered measurable SQL tuning gains (30s to 2s, 70% CPU reduction).”

“Backend engineer who significantly evolved an internal analytics/reporting platform (Python API + Postgres) powering self-service dashboards for product/business teams, focusing on reliability under heavy concurrent load and fast query performance. Demonstrates strong production engineering practices across API design (FastAPI), observability, incremental rollouts with feature flags, and data security using JWT/RBAC plus Postgres row-level security.”

“Backend engineer from Emphasis who modernized legacy, tightly coupled workflow systems into observable, event-driven microservices using Kafka. Led a monolith-to-microservices refactor with shadow traffic, feature flags, canary rollout, dual writes, and reconciliation, and strengthened reliability with idempotent consumers, DLQ/replay, and an outbox pattern to prevent DB/event inconsistency. Strong focus on secure multi-tenant APIs (OIDC/JWT, RBAC/ABAC, Supabase-style RLS) and frontend enablement via OpenAPI and typed client generation.”

“Backend engineer with experience at Discover, Dell, and Carpus building high-concurrency microservices and secure APIs. Delivered measurable impact in fintech workflows by integrating credit bureaus (TransUnion/Experian), cutting loan processing from days to minutes and reducing latency 65% through PostgreSQL tuning and caching. Strong in production security patterns (JWT/RBAC, Postgres row-level security for multi-tenant isolation) and low-risk migrations (shadow mode + incremental rollout).”

“LLM/agentic systems practitioner who repeatedly takes customer-facing LLM prototypes into production by operationalizing prompts, hardening RAG pipelines, and adding monitoring/guardrails. Has hands-on experience debugging intermittent production failures under high traffic (vector store timeouts/empty retrieval) and implementing fail-safe behavior plus alerting. Also partners closely with sales in pilots/POCs, customizing demos with customer data and running side-by-side comparisons to drive adoption.”

“Healthcare-focused LLM engineer who deployed a production triage and clinical knowledge retrieval assistant using RAG and LangGraph-orchestrated multi-agent workflows. Emphasizes clinical safety and compliance with robust hallucination controls, HIPAA/PHI protections (tokenization, encryption, audit logging, zero-retention), and human-in-the-loop escalation; reports a 75% latency reduction in a healthcare agent system.”

“Built an agentic, multi-step LLM system that generates full-stack code for API integrations using LangChain orchestration, Pinecone/SentenceBERT RAG, and a human-in-the-loop feedback loop for iterative code refinement. Also collaborated with non-technical content writers and PMs during a Contentstack internship to deliver a Slack-based AI workflow that generates and brand-checks articles with one-click approvals.”

“Built a production multi-agent orchestration platform to automate healthcare claims and HR workflows, combining LangChain/CrewAI/AutoGPT with RAG (FAISS/Pinecone) and fine-tuned open-source LLMs (LLaMA/Mistral/Falcon) in private Azure ML environments to meet HIPAA requirements. Emphasizes rigorous agent evaluation/observability (trajectory eval, adversarial testing, LLM-as-judge, drift monitoring) and reports measurable outcomes including 35% faster claims processing and 40% fewer chatbot errors.”

“Telecom (Verizon) AI/ML practitioner who built a production multimodal system that ingests messy customer issue reports (calls, chats, emails, screenshots, videos) and turns them into confidence-scored incident summaries with reproducible steps and evidence links. Also built KPI/alarm-to-ticket correlation to rank likely root-cause domains (RAN/Core/Transport), cutting triage from hours to minutes and improving MTTR.”

“At BondiTech, built and deployed customer-facing backend improvements for enterprise dashboards handling 1M+ records, redesigning a .NET/Entity Framework API with server-side pagination/filtering and feature-flagged rollout to cut latency from ~15s to ~2s. Experienced integrating customer systems into existing APIs, including stabilizing a legacy CRM sync by normalizing inconsistent IDs, handling strict rate limits with batching, and adding DLQs plus reconciliation reporting.”

“Machine Learning Intern at Docsumo who delivered a customer-facing fraud-detection solution end-to-end: rebuilt the pipeline, deployed a Random Forest model, and shipped a Python/Flask microservice on AWS SageMaker. Drove measurable production impact (precision +30%, processing time cut in half, manual review -60%, customer satisfaction +15%) and demonstrated strong customer integration and live-incident response skills.”

“JavaScript engineer with Walmart experience contributing to the Yup validation library—reproduced a nested-object validation bug, fixed merge logic, and added test coverage. Strong in systematic debugging/performance isolation (DevTools + timing logs), plus end-to-end ownership including documentation, monitoring, and issue triage.”

“Backend/infra-focused software engineer who built an autonomous Python API-orchestration agent using asyncio with strong reliability and observability (trace IDs, structured logs, retries/timeouts) and containerized dev workflow. Experienced deploying Python services to Kubernetes with Helm and running GitOps CI/CD via ArgoCD, plus leading an AWS IAM-to-Identity Center migration using CloudTrail-driven least-privilege role design. Also built and debugged a Kafka/SnapLogic bidirectional pipeline syncing Redshift and HBase, resolving missing-record issues via Kibana-driven investigation.”

“AI/ML engineer with forensic analytics and healthcare claims experience (Optum), building production LLM/RAG systems to surface context-driven fraud patterns from unstructured claim notes and explain risk to investigators. Strong in large-scale retrieval performance tuning, legacy API integration with reliability patterns (SQS, circuit breakers), and MLOps orchestration on Airflow/Kubernetes with rigorous testing, monitoring, and stakeholder-friendly interpretability.”