Vetted Next.js Professionals

“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.”

“Full-stack engineer who built and shipped an analytics dashboard for search visibility using Next.js App Router/TypeScript with a server-components-first data strategy and server actions for interactivity. Designed and optimized the underlying Postgres analytics model and queries at scale, and implemented a durable Temporal-based indexing workflow with retries and idempotency—plus delivered a major frontend performance jump (Lighthouse low 70s to mid-90s).”

“Full-stack engineer with startup experience who owned an end-to-end rebuild of a production analytics page at VideoNest (Next.js/TypeScript frontend, FastAPI/Python backend, Postgres), including third-party data ingestion/sync and query/index optimization; the feature reached 2,500+ users and received positive feedback from large clients. Also built a habit/community mobile app (Celeri) with near-real-time step updates using polling and UI optimizations like pagination and selective re-rendering.”

“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.”

“Entry-level software engineer who built an agentic AI backend in Python/FastAPI, including APIs for conversation history retrieval and user data storage, and worked through async/concurrency challenges for multiple agents querying simultaneously. Also has practical AWS experience using S3 for static hosting with Lambda and RDS for backend/data access.”

“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).”

“Frontend engineer who has led React + TypeScript products end-to-end, emphasizing scalable architecture (custom hooks, compound components) and quality via strict typing, CI, and code review standards. Experienced shipping complex multi-step workflows with strong performance practices (virtualization, code splitting, RUM) and de-risked rollouts using feature flags, monitoring, and rapid iteration in collaboration with product/design.”

“AI/LLM practitioner at iFrog Marketing Solutions who drove a RAG chatbot from prototype to production in a legacy, AI-resistant environment by validating customer needs and building a business case. Implemented production-grade LLM practices (CI/CD eval gating, rollbacks, prompt/context engineering) and led internal workshops to bring non-AI-native developers up to speed while partnering with sales on tailored demos to drive adoption.”

“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.”

“Frontend engineer who has led end-to-end builds for Toronto Insights properties, including a City Council voting records site and a business directory dashboard. Uses React/Next.js with TypeScript and Tailwind, sets up CI quality gates with GitHub Actions, and optimizes performance via Next.js caching and server components while collaborating closely with designers to translate Figma into polished, responsive UI.”

“LLM/agent builder who has shipped production AI systems in the wellness space, including an LLM-powered food tracking product used by 5000+ users and a voice/call-routing onboarding workflow using LangGraph/LangChain with LiveKit and Twilio. Strong focus on practical reliability work: latency reduction, retrieval/embedding tuning, and CI-driven evaluation with simulations and metrics.”

“Frontend engineer with experience delivering complex, data-heavy React + TypeScript dashboards in financial services (Morgan Stanley), including React 18 migration and rigorous quality practices (~80% test coverage). Also improved an existing collaboration product (Heycollab) by reducing duplication and boosting performance ~30% using component modularization, API optimizations, code splitting, and virtualization; experienced with phased rollouts and feature flags for risk-sensitive releases.”

“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.”

“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.”

“Backend engineer who built and optimized a high-traffic e-commerce platform in Python/Flask, focusing on scalability and reliability through service decomposition, Redis caching, and Celery-based background processing. Also integrated an AI intent-classification chatbot as a separately deployable inference service on AWS and has hands-on experience designing multi-tenant data isolation strategies in PostgreSQL.”

“JavaScript/Node.js engineer with open-source contribution experience (Mongoose) focused on connection pooling, test reliability, and memory/resource management. Has diagnosed and fixed real-world performance issues in an insurance claims application and improved resilience via failover DB design. Also experienced producing compliance/governance documentation for an EU-based biopharma, enabling stakeholders to make decisions quickly amid changing regulations.”

“Backend-leaning full-stack engineer who ships AI-enabled products end-to-end: built CodeChat, a production internal codebase Q&A tool using RAG with Pinecone and a model-agnostic wrapper across OpenAI/Anthropic/AWS Bedrock, cutting AWS costs ~50% and latency ~45%. Also built and operated RealityStream, a Flask-based real-time forecasting API with JWT/RBAC, MLflow model versioning, and Prometheus/Grafana observability, including handling a real production latency incident via rollback, preloading, and caching.”

“Product-focused full-stack engineer who built and shipped an LLM-powered document-to-flashcard conversion pipeline end-to-end (backend + React/TypeScript UI) in ~10 days. Experienced with event-driven queue/worker systems (Redis/BullMQ), PostgreSQL performance tuning, and AWS production operations, including resolving real scaling incidents and driving reliability from ~70% to nearly 100%.”

“QA engineer with Citibank experience owning mobile automation and cross-platform validation (Android/iOS), including push notifications, RBAC, and backend API/data sync checks. Demonstrates strong Cypress/JavaScript E2E expertise—stabilizing CI-flaky React tests via cy.intercept—and builds pragmatic GitLab CI pipelines with smoke/regression gating plus rich reporting (Cypress Dashboard, Slack).”

“Early engineer at a fast-growing startup who owned an AI-powered portfolio/site generation workflow end-to-end (frontend in Next.js App Router/TypeScript through backend orchestration). Emphasizes server-first security/performance (Server Components/Actions, revalidation), and production hardening with validation, caching, observability, retries/idempotency, and CI/E2E testing.”

“Built and shipped a production RAG-based assistant that lets parents ask natural-language questions about their child’s learning progress, using pgvector retrieval (child-id filtered) and Redis caching to hit ~180ms latency. Implemented real-world guardrails and compliance (Llama Guard, COPPA, retrieval thresholds, fallbacks) with 99.5% uptime, and ran human-in-the-loop eval loops that improved satisfaction from 3.8 to 4.2 while serving 60k+ monthly users and reducing costs significantly.”

“Former co-founder at PreConception (acquired) who partnered closely with Operations, Legal, and Medical teams to deliver a HIPAA-compliant product meeting technical and regulatory requirements. Motivated by mission and team fit, and interested in a Venture Studio CTO path with a focus on 0-to-1 building and early validation via beta testing/PMF.”

“Backend/full-stack engineer from Foxconn Industrial Internet who led development of a production TypeScript/Node.js facility monitoring platform delivering near real-time manufacturing metrics (e.g., downtime and OEE) using MySQL + InfluxDB and a React dashboard. Demonstrates strong production operations mindset with queue-based workers, idempotency/DLQ patterns, structured observability, and automated Docker + GitLab CI/CD deployments.”

“Founding member of Tausight, building AI systems to detect and protect PHI for healthcare organizations; helped take the company through post–Series A funding and exited after ~6 years. Drove a strategic collaboration with Intel’s OpenVINO team—becoming the first to deploy it in a real production system and improving model performance by ~30% on customer Intel-CPU machines.”