Vetted Integration Testing Professionals

Kruti Mehta

Screened ReferencesStrong rec.

Mid-level Software Engineer specializing in backend systems and FinTech analytics

Maryland, USA5y exp

“Engineer with a pragmatic, high-leverage approach to AI-assisted development: uses AI and multi-agent workflows aggressively for implementation and internal tooling, while maintaining strict human oversight for user-facing features. Stands out for treating agents like junior engineers, breaking work into actionable tasks, and combining robust testing, local E2E validation, and feature-flag rollouts to safely ship production code.”

“AI/back-end engineer at Canon who helped build and operate an internal production LLM platform that acts as a secure middle layer between users and models, defending against jailbreaks/prompt injection while enabling RAG, memory, and grounded responses over company data. Experienced with LangChain/LangGraph orchestration, vector DB retrieval, and reliability practices (testing, monitoring, adversarial prompts) to run high-throughput, low-latency AI workflows in production.”

Snigdha Reddy Podduturi

Screened ReferencesStrong rec.

Junior Data & AI Engineer specializing in cloud AI and analytics

Remote3y exp

“Built production AI backend systems in healthcare and e-commerce, including a healthcare agent that automated clinical workflows like medication refills, immunizations, and scheduling using FHIR APIs and cloud-native infrastructure. Strong in end-to-end backend ownership, LLM orchestration, and adding guardrails/validation for high-stakes and customer-facing AI workflows.”

Phat Dang

Screened ReferencesStrong rec.

Junior Software Engineer specializing in full-stack, DevOps, and GenAI

Toledo, OH2y exp

“Robotics software engineer with hands-on hardware integration who built an AI-enabled smart dog door using a Raspberry Pi, camera-based recognition (DeepFace adapted for dogs), and stepper motor control (TB6600/NEMA 17). Experienced in ROS/ROS 2 across perception-to-controls, rigorous bag-driven debugging of SLAM/navigation issues, and deploying robot software with simulation-in-the-loop testing plus Docker/Kubernetes CI/CD.”

Yumna Khan

Screened ReferencesStrong rec.

Junior Frontend Engineer specializing in React, Next.js, and TypeScript

Remote2y exp

“Frontend lead on a SaaS platform who migrated the product from React/JavaScript to Next.js/TypeScript, improving Web Vitals (including LCP), SEO, and navigation performance. Strong focus on quality and scalability via automated testing and SonarQube quality gates, plus reusable component architecture (e.g., a shared table used across ~20 pages). Built complex analytics dashboards and an AI workflow drag-and-drop canvas UI using React Flow with iterative stakeholder-driven delivery.”

Nobin Johnson

Screened ReferencesStrong rec.

Mid-Level Software Engineer specializing in full-stack, cloud, and mobile development

Buffalo, NY4y exp

“Backend engineer with open-source web3.js contributions focused on preventing costly smart-contract execution failures, plus hands-on blockchain startup experience scaling an NFT mint from a test EC2 setup to AWS Lambda/API Gateway under extreme concurrency. Built a multiplayer backend improving tick rate from 40 Hz to 105 Hz using UDP, containerization, and Kubernetes, and has healthcare production support experience across 10+ apps. Also publishes IEEE research papers and produces developer-friendly documentation that accelerates team adoption.”

“Built and shipped a production Python-based agentic RAG document retrieval system over 80K records using FastAPI, OCR, vector search, and AWS infrastructure, with a strong emphasis on reliability, testing, and observability. Stands out for treating AI failures like production incidents—turning hallucinations, retrieval misses, and OCR issues into regression tests—and for quantifiably reducing document lookup time from about 12 minutes to under 90 seconds.”

“Backend engineer focused on real-time, event-driven distributed systems (Node.js/TypeScript) with strict latency and reliability requirements. Deep hands-on experience debugging concurrency issues and designing resilient workflows (idempotency, circuit breakers, compensating actions) with strong observability; familiar with ROS/ROS2 concepts and confident ramping into robotics integrations.”

“Built and productionized an LLM-enhanced version of the WeDAA platform to auto-generate microservice architecture diagrams and support code generation from user prompts, including a practical solution for non-overlapping canvas object placement via coordinate templates. Experienced in diagnosing agentic workflow failures using AWS Strands agents with feature-flagged debug logging, and frequently supports sales through tailored demos and POCs to drive adoption.”

“Built and deployed a production LLM-assisted recommendation and insights platform that unifies structured, semi-structured, and unstructured data via a modular ingestion pipeline, canonical schemas, embeddings, and late-fusion modeling. Experienced in operationalizing ML/LLM systems with Airflow and Kubernetes (Dockerized services, autoscaling, rolling updates) and emphasizes reliability through layered testing, guardrails, monitoring, and A/B experimentation while partnering closely with non-technical stakeholders.”

“Frontend engineer who led Curion’s end-to-end UI architecture for a real-time meetings/elections product, emphasizing scalability, testing, and CI/CD. Has built complex React+TypeScript dashboards (Redux) and improved a Flutter codebase with DDD and dependency injection, enabling smoother third-party swaps like Zoom to LiveKit and rapid delivery of Brazil-specific payment methods (PIX/Boleto).”

“QA Automation Engineer with hands-on ownership of an end-to-end Selenium-Java/TestNG suite for an e-commerce platform, integrated into Jenkins with PR smoke and nightly regression gating. Notably caught and validated (via SQL/API checks) a critical defect where checkout reported success but orders were not persisted, preventing customer-impacting production issues; also has Cypress experience stabilizing flaky UI tests using robust selector and wait strategies.”

“Full-stack engineer at an early-stage EdTech startup building an AI-tutoring product; owns most of a Django REST backend, CI/CD, and key customer-facing features like FERPA-compliant auth, subscription payments, and real-time LaTeX input/rendering. Also built a /rPlace-style real-time multiplayer canvas (PolyPlace) using microservices, WebSockets, and event sourcing, with performance-focused client rendering (zoom/pan/viewport-based updates) and stress testing.”

“Full-stack/product-leaning engineer in logistics and high-traffic portals who ships production AI features: built an AI-assisted shipment status Q&A system using Pinecone + GPT-4 and a high-volume Python ingestion pipeline (500K+ records/day), delivering 35% fewer support tickets and cutting resolution time from 11 to 4 minutes. Also led a legacy Angular-to-React/TypeScript rebuild that boosted Lighthouse performance from 60 to 90, and has hands-on AWS EKS operations experience including resolving a 3x traffic scaling incident.”

“Software engineer with enterprise CPQ/CRM/ERP integration experience (Argano) who owned an end-to-end pricing preview capability deployed on AWS Kubernetes with Jenkins CI/CD and full observability (Prometheus/Grafana). Also built an AI-native research agent using LangChain + Chroma to filter academic papers, reporting ~15 hours/week saved for a professor.”

“Full-stack engineer who has delivered production customer analytics/dashboard features using Next.js App Router + TypeScript on the frontend and Java Spring Boot microservices on the backend. Demonstrates strong production ownership (monitoring latency/error rates/adoption) plus hands-on performance work across React rendering and Postgres query/index optimization, and has implemented Temporal-like durable workflows with retries and idempotency.”

Hafiz Muhammad Zeshan

Screened

Senior Full-Stack Engineer specializing in MERN, AWS, and scalable SaaS platforms

Victoria, Australia5y exp

“Frontend lead for B2B SaaS products, owning React + TypeScript architecture end-to-end and scaling complex dashboards/workflows with a feature-based structure, shared design system (Tailwind), and strong quality automation. Experienced shipping high-impact features quickly using incremental delivery, feature-flagged rollouts, and performance profiling/optimization to keep production stable as usage grows.”

Prince Gupta

Screened

Senior QA Lead / Release Manager specializing in web, mobile, and OTT platforms

Mumbai, India8y exp

“Game QA professional working in an agile studio environment with end-to-end ownership of feature testing, including an AAA release enhancement focused on user authentication and session management. Experienced partnering with PM/design/dev from PRD walkthroughs through triage, regression, and release readiness, and has implemented repeatable coverage via TestRail/Jira traceability plus Playwright (TypeScript) automation.”

“Unity/C# tech lead specializing in multiplayer systems and networking reliability: built a transaction/prediction-based multiplayer inventory system using snapshots/deltas that eliminated duping and supports very large item transfers via RPC. Also implemented an LLM-driven localization pipeline (SGLang on DGX Spark with Gemma Translate) with automatic OpenAI fallback and human QA, and has shipped VR work on Meta Quest 3 with cross-platform input abstraction (mobile/VR/web).”

“Independent builder of production-grade systems: shipped an end-to-end URL shortener with JWT auth, Redis rate limiting/caching, Postgres, Docker, and real-time analytics, and separately architected a Redis-backed distributed task queue handling 1000+ tasks/min. Demonstrates strong distributed-systems instincts (atomicity, retries/DLQ, idempotency, heartbeats) plus a focus on maintainable code and self-documenting APIs (FastAPI/OpenAPI, versioned routes).”

“Backend/AI engineer who built production-grade intelligence systems in high-stakes domains including tax/legal document analysis and brain tumor MRI workflows. Stands out for combining LLM/RAG product delivery with strong engineering rigor around retrieval evaluation, grounding, validation, observability, and safe fallbacks—turning impressive demos into systems users could actually trust.”