Vetted Vercel Professionals

Satya Kumar Itekela

Screened ReferencesStrong rec.

Senior Frontend Engineer specializing in high-performance React/Next.js web apps

Remote, Canada8y exp

“Frontend engineer with experience at Autodesk and Quantify, leading and scaling Next.js/React + TypeScript products from architecture through QA. Strong focus on performance (Core Web Vitals, ISR, caching/CDN) and real-time interfaces (WebSockets, Chart.js/D3), with measurable wins like 30–40% bundle reduction and ~60% less data overfetching using GraphQL/Apollo.”

“Frontend-focused engineer (~8 years) with strong React/TypeScript and Angular experience who leads end-to-end UI architecture, performance budgets, and quality practices at scale. Known for restructuring large, tightly coupled codebases into feature-based domains to eliminate UI freezes and improve team throughput, and for tight Product/Design/QA alignment via a “3 Amigos” process that catches workflow gaps early. Currently seeking a new frontend role due to GE Vernova outsourcing; has been acquired twice by large companies.”

Manyuvraj Sandhu

Screened

Mid-level Full-Stack Developer specializing in Next.js, AI-driven apps, and payments

Calgary, AB4y exp

“Frontend engineer who has led complex React + TypeScript products end-to-end, including a real-time canvas-based digital signature editor and a multi-step AI workflow dashboard. Demonstrates strong architecture and performance instincts (state machines for streaming async updates, bundle/render optimizations) plus pragmatic shipping practices (feature flags, automated tests, analytics and user interviews), with a quantified impact from refactoring (~30% less duplicated UI code).”

Anthony Odinukwe

Screened ReferencesStrong rec.

Senior Full-Stack Engineer specializing in React/Next.js web applications

Calgary, Canada6y exp

“Frontend-focused engineer who has led end-to-end delivery for an ecommerce web app and built complex React + TypeScript dashboards with real-time data and multi-step workflows. Strong in scalable architecture (typed API layers, shared hooks, design systems), quality at scale (Jest/RTL + Playwright), and performance optimization (virtualization, lazy-loading, memoization). Experienced shipping high-impact checkout changes via feature-flagged rollouts with metric/error monitoring and rapid iteration.”

“Built and productionized an LLM-driven document verification workflow for a construction firm’s submittals process, moving from a Vercel/Next.js prototype to a FastAPI + LangChain/LangGraph backend with background workers and multi-server deployment. Uses LLM tools (e.g., OpenAI Codex/Cloud Code) for rapid development and log-driven root cause analysis, and partners with customer teams on evaluation metrics and iterative improvements.”

“ML/AI engineer with hands-on experience shipping production computer vision and GenAI systems, including a fabric defect detection platform that combined vision models with agentic LLM workflows to reach 89% human-inspector agreement at 200 ms latency. Also built a RAG-based code QA tool for developers and emphasizes production monitoring, evaluation, caching, and reusable Python service design.”

“Enterprise-minded builder who has owned complex, high-impact systems from discovery through stabilization, including a customer master data platform at AB InBev serving 2,000 sales reps across 13 countries. Also demonstrates strong AI product instincts, having built a first-place ReAct-style NYC property intelligence agent at IBM's AI Demystified Hackathon, while showing deep rigor in data quality, integrations, and production reliability.”

“LLM/RAG engineer who took a PDF-heavy agent from prototype to production for an Africa-based client, combining Pinecone retrieval with robust PDF parsing (unstructured.io, OCR, structured table extraction). Demonstrates strong production mindset (eval metrics, prompt hardening, security/scalability) and measurable optimization impact (30% efficiency gain, 2x faster responses), and has helped close deals by building security-focused POCs for skeptical IT stakeholders.”

“Built and iterated an end-to-end virtual waiting room for a real-time ticketing prototype, making concrete architecture tradeoffs (polling + Redis Pub/Sub) and improving performance post-launch with Redis caching (+30% throughput, -15% p99 latency). Also has hands-on experience building Spark/HDFS ETL pipelines with strong reliability/observability patterns and running disciplined NLP model evaluation loops on review-rating classification.”

“Backend engineer with experience at Roche and Jarsy focused on API and data-layer performance. Re-architected slow generalized endpoints into more efficient APIs (30% faster lookups) and led a schema refactor/migration with feature-flag rollout, dual writes, rollback scripts, and automated integrity checks; also addressed pipeline duplicate-entry issues via deduplication.”

“Built and scaled a multi-agent LLM automation pipeline during a fintech internship, growing from a rapid 1-week proof-of-concept to a 15+ agent hierarchical system that cut market brief report generation time from ~5 hours to under 30 minutes. Hands-on with agent frameworks (Haystack, CrewAI, LangChain) and experienced in debugging agent communication issues via sandboxed modular testing and context/token management; also regularly gives architecture-first technical demos at multiple hackathons and university events.”

“Backend/platform engineer who has owned end-to-end production systems in financial/claims domains, including a transaction analytics microservice platform processing ~10M daily operations and cutting latency from ~150ms to <70ms. Also productionized an LLM-powered monitoring/alerting capability (Llama 3 + FastAPI) with prompt design, guardrails, and production evaluation, and led monolith-to-microservices modernization on AWS using feature flags and parallel runs.”

Divyansh Agarwal

Screened

Junior Machine Learning Engineer specializing in computer vision and LLM applications

New York, NY3y exp

“Built and led an autonomous driving software effort for Formula Student, owning the full autonomy stack (perception, planning, control) orchestrated in ROS. Implemented stereo depth + YOLO object detection, RRT/RRT* planning, and a robust SLAM pipeline (Kalman filter, submapping) while leveraging Gazebo simulation and modern deployment tooling (Docker/Kubernetes, AWS, GitHub Actions CI/CD).”