Vetted OAuth 2.0 Professionals

“Backend engineer at Discover who built and scaled Python/Flask services for a card dispute resolution platform, tackling long-running external network validations with Celery+Redis and delivering measurable gains (response time ~3s to <300ms; throughput +40%). Experienced in high-scale PostgreSQL/SQLAlchemy optimization (partitioning, read replicas, N+1 avoidance), event-driven systems with Kafka, and integrating ML fraud detection using AWS SageMaker/Lambda/ECS with clear separation of real-time vs batch processing.”

“Backend/platform engineer who owns policy-lifecycle workflow microservices built in Python/FastAPI with async + DDD, Kafka event processing, SQLAlchemy, JWT/RBAC, and Redis caching (cut DB load ~40%). Experienced deploying Java and Python microservices to Kubernetes with Helm and GitOps (ArgoCD) plus Jenkins/GitHub Actions pipelines to AWS/ECR, and has supported phased on-prem-to-cloud migrations with dependency mapping and data consistency strategies.”

“Built and owned a hackathon project (Gritto) with a Python/FastAPI backend that routes user text through a sequence of Gemini agents to produce structured JSON outputs. Has hands-on production deployment experience using Docker/Docker Compose, GitHub Actions CI/CD, AWS App Runner, MongoDB, and secrets management (Doppler + migration to AWS Secrets Manager), plus implemented a chat-like experience via multiple HTTP requests when SSE wasn’t viable.”

“Backend engineer with Morgan Stanley experience building and owning an end-to-end Python FastAPI microservice for high-volume market data used by trading and risk systems. Strong in performance tuning and reliability (PySpark, Redis caching, async APIs), real-time streaming with Kafka, and production operations (Docker/Kubernetes, GitOps-style CI/CD, monitoring). Has led cloud/on-prem migration work across AWS and Azure, including fixing Azure Synapse performance issues via query and pipeline redesign.”

“Hands-on backend/AI engineer who solo-built two production Claude-based agent systems: an internal Slack RAG over Confluence/Jira/code/regulatory docs and a HIPAA/GDPR-compliant patient chatbot with embedding guardrails and expert-in-the-loop evals. Also architected a multi-region patient portal + microservices platform with Terraform/CI-CD and federated gateways, delivering major onboarding automation and strong reliability wins (PgBouncer, chaos/perf testing).”

“Backend engineer with American Express experience maintaining an internal Python/Flask rewards simulation microservice used by product analysts and QA. Demonstrated strong performance and scalability work: moved batch simulations to Celery, added Redis caching to cut DynamoDB latency, and tuned Postgres/SQLAlchemy queries with EXPLAIN ANALYZE and composite indexes (bringing API responses under ~200ms by queueing jobs). Also has experience integrating ML via Flask-based model-serving APIs (scikit-learn/LightGBM packaged with joblib) and designing multi-tenant data isolation and tenant-specific configuration systems.”

“Full-stack engineer with hands-on ownership of a real-time loyalty rewards notification system at Dell, spanning React UI, Spring Boot/Node microservices, Kafka event processing, and Oracle/Postgres persistence. Strong production operations experience across AKS/Azure DevOps and AWS (EC2/RDS/S3, autoscaling, CloudWatch), including resolving peak-load Kafka lag and API latency incidents through scaling and performance tuning.”

“Full-stack engineer with strong production ownership in banking/identity & entitlements systems, building Spring Boot + Postgres/Redis services and React dashboards, then deploying on AWS EKS with Jenkins CI/CD. Demonstrated impact through reduced authorization latency and fewer access-related support tickets, plus strong observability and reliability practices (CloudWatch, tracing, autoscaling, Kafka pipelines with DLQs and reconciliation).”

“Software engineer with experience building and iterating high-volume Spring Boot microservices on AWS (Docker/Kubernetes) and integrating with React front-ends. Also delivered an LLM-powered document summarization system using embeddings + retrieval (RAG) with grounding/guardrails and built evaluation loops that directly drove retrieval and chunking improvements. Has scaled Kafka-based pipelines processing millions of messy financial/infrastructure records with reliability and cost/latency tradeoff management.”

“Full-stack engineer who built a cloud storage app feature (file upload/management) with Next.js App Router + TypeScript and owned post-launch improvements. Also has internship experience building a geospatial AI chatbot: designed Postgres/PostGIS data models and optimized spatial queries, and implemented an LLM workflow orchestrated with LangChain/LangGraph plus a RAG pipeline grounded in OpenStreetMap data to reduce hallucinations.”

“Backend engineer with healthcare domain experience building AI-driven radiology workflow systems. Evolved tightly coupled APIs into secure, reliable FastAPI-based services by moving heavy imaging/data processing into idempotent asynchronous pipelines with retries, feature-flagged incremental rollout, and strong data-integrity controls (constraints, backfills, validation). Strong focus on defense-in-depth security for sensitive patient data (OAuth2/JWT, RBAC, and database-level protections).”

“Backend engineer with hands-on experience modernizing high-volume transactional systems by decomposing monoliths into Spring Boot microservices on AWS, using Kafka for async workflows and Redis/SQL tuning for latency. Has built Python/FastAPI services with strong API contracts and production-grade security (OAuth2/JWT, RBAC, row-level security), and proactively hardened payment flows against race conditions and double-charging via idempotency.”

“Backend/data platform engineer who has built and operated a cloud-native media ingestion/processing platform in Python (Django/DRF, FastAPI) with Kafka, Postgres, and Redis, emphasizing multi-tenant security and reliability. Delivered AWS production systems combining EKS and Lambda with Terraform + GitHub Actions/Helm, and built Glue-based ETL pipelines with strong schema-evolution and data-quality practices; also modernized SAS analytics into Python on AWS. Seeking fully remote roles with a $120K–$140K base range.”

“Backend/data platform engineer who owned end-to-end production services for a fleet analytics/GenAI platform, spanning FastAPI microservices on Kubernetes and AWS (EKS + Lambda) event-driven workloads. Strong in reliability/observability (OpenTelemetry, circuit breakers, idempotency), data pipelines (Glue/Airflow/Snowflake), and measurable performance/cost wins (SQL 10s to <800ms P95; ~30% compute cost reduction).”

“Software engineer who built and owned a centralized Asset Management platform at Hyundai Mobis used daily by multiple internal teams, delivering everything from UI/TypeScript React frontend to Spring Boot services, databases, deployment, and post-release support. Experienced in scaling microservices with Kafka-based async workflows, improving data consistency/real-time updates, and implementing reliability patterns like standardized API contracts, retries, DLQs, and targeted caching.”

“Backend engineer focused on AI-enabled systems, having built a production-style RAG pipeline (vector search + LLM) exposed via Python/Flask endpoints with strong observability and hallucination-reduction techniques. Demonstrates deep performance work in PostgreSQL/SQLAlchemy (5x faster analytics queries) and high-throughput optimization using Celery + Redis (800ms to 120ms latency, 3x throughput), plus schema-per-tenant multi-tenancy with tenant-aware middleware and logging.”

“Software engineer with deep healthcare claims domain experience who has owned customer-facing portals end-to-end (Java/Spring Boot + React/TypeScript) and improved usability/performance based on real user feedback. Built microservices using REST and RabbitMQ with strong observability (Splunk/cloud metrics), and delivered an internal claims investigation dashboard that streamlined operations through centralized data, search, and filtering.”

“Software engineer experienced owning internal, customer-facing dashboards and internal ops tools end-to-end, emphasizing fast iteration without sacrificing stability (CI/CD, automated tests, feature flags, monitoring). Built a TypeScript/React role-based dashboard backed by Java Spring Boot and has hands-on microservices experience with RabbitMQ, including production hardening with retries, dead-letter queues, logging, and health checks.”

“Robotics software engineer who built the software layer for an autonomous warehouse sorting system, spanning navigation/path planning, task scheduling, and backend services. Deep hands-on ROS 2 Foxy experience (Nav2/costmaps) and real-time multi-robot debugging, using simulation-driven analysis plus incremental/partial re-planning to handle dynamic obstacles in production-like warehouse environments.”

“Backend/ML systems engineer who built a Flask + PostgreSQL internal ticketing platform and demonstrates strong database/ORM performance depth (indexes, partitioning, RLS multi-tenancy). Notably optimized a high-throughput attachment OCR/embedding pipeline with batching, deduplication, and Redis caching, cutting median latency from 45s to 10s and reducing worker cost by 35% while increasing throughput 4x.”

“JavaScript/React performance-focused engineer who contributed upstream to an open-source virtualization/pagination library, fixing overlapping-fetch race conditions and introducing prefetch/deduping patterns that cut load times from ~3s to <900ms and reduced render thrash ~35%. Also built healthcare automation systems (clinical summary and claims triage), including a FastAPI + RAG pipeline that retrieved CPT/ICD evidence, improving decision accuracy from 67% to 86% and reducing turnaround time by 40%.”

“Software/full-stack engineer focused on deploying and integrating microservice applications into production AWS and hybrid cloud/on-prem industrial environments. Demonstrated end-to-end troubleshooting by tracing intermittent user failures to network routing/packet loss caused by load balancer and NIC misconfiguration, then adding monitoring to prevent recurrence. Also delivers customer-specific Python extensions with strong validation, testing, and backward compatibility.”