Vetted REST APIs Professionals

“Backend engineer (IpserLab) who owned Python services for a production quiz/analytics platform, focusing on reliability and low-latency behavior under peak load. Hands-on with Kubernetes + Docker deployments and GitHub Actions CI/CD in a GitOps-style workflow, including solving configuration drift and enabling fast rollbacks. Also implemented Kafka-based event streaming with idempotent consumers and strong observability (lag tracking, structured logging, alerting).”

“Frontend engineer who built a live sports games hub end-to-end: ingesting and normalizing data from 4 external APIs, populating a high-volume live table, and delivering a real-time WebSocket-driven React/TypeScript dashboard. Strong on scalable architecture (SOLID, layered design, queues), performance/load testing with Docker, and startup-style ownership across code review, QA, and staged rollouts to web and app stores.”

“Frontend engineer who led end-to-end delivery of a Vite/React/GraphQL/MUI SPA with feature-based, layered architecture and strong quality gates (TypeScript strict, CI, testing, a11y). Built a large analytics dashboard using microfrontends and multiple state management approaches, including integrating with a legacy PHP host via window-object communication, and managed safe rollouts with LaunchDarkly plus Datadog usage/session-replay monitoring.”

“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).”

“Frontend engineer (Capgemini experience) who has led products end-to-end and improved existing codebases for scalability and performance. Built complex React + TypeScript dashboards with real-time, multi-API data and stabilized them via centralized data handling, strict typing/validation, and performance techniques like debouncing/memoization. Emphasizes practical standards, incremental refactoring, and tight collaboration with design/product and QA to ship reliably under deadlines.”

“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.”

“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.”

“Backend/platform engineer with payments and insurance domain experience (Cognizant), owning high-volume production systems end-to-end. Shipped a Spring Boot payment tokenization service with strong observability and phased migration that cut transaction latency ~30% and improved payment efficiency ~25%. Also productionized an ML-driven financial health/risk analytics pipeline with near real-time dashboards across 70+ schools, emphasizing interpretability, data quality, and drift monitoring.”

“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.”