Vetted Next.js Professionals

Anthony Thanpoovong

Screened

Entry-level Software Engineer specializing in cloud, AI, and full-stack development

Toronto, Canada1y exp

“Backend/AI engineer with hands-on experience building LLM-powered data products and AI platform workflows, including a project that turns tabular datasets into graphs, summaries, and chat-based insights with 1-2 second latency. Also contributed at TELUS to a Sovereign AI Factory self-serve onboarding platform tied to 100+ NVIDIA H200 GPUs, giving them an interesting mix of applied LLM, platform, and infrastructure exposure.”

Paavani Karuturi

Screened

Junior Backend and ML Engineer specializing in distributed systems and LLM infrastructure

San Jose, CA2y exp

“Backend engineer with strong ownership across authentication, API infrastructure, and AI-powered document workflows. They built and operated a production auth microservice supporting 10,000+ users with measurable latency and security improvements, and also shipped hackathon and applied-AI systems including legal document and medical document retrieval/Q&A products.”

“Frontend-leaning full-stack engineer with experience building real-time, high-stakes operational software for airport gate management and billing/analytics systems. Stands out for combining strong React/TypeScript architecture with backend and data-layer ownership, including WebSockets, SQL optimization, and analytics feature delivery in production.”

“Candidate approaches AI-assisted coding like a senior developer supervising junior contributors: they define precise technical requirements, enforce code quality and documentation, and review outputs before approval. They also actively lead multi-agent workflows using OpenClaw and a Kanban-style AI project management setup, coordinating both coding and non-technical agents.”

“Full-stack software engineer who has owned production workflows spanning React/Next.js, FastAPI, Redis-backed async processing, and PostgreSQL in a multi-tenant invoice-processing product. Shows strong product instincts as well—improving UX for long-running operations, iterating MVPs based on real user behavior, and balancing reusable abstractions with practical implementation constraints.”

“Entry-level backend engineer currently building AI infrastructure for a legal-tech product, including message APIs between frontend and LLMs and MCP-based integrations to enterprise legal systems. Stands out for owning backend components end-to-end in an ambiguous early-stage environment and for resolving a critical pre-demo bug under intense time pressure.”

“Full-stack engineer focused on AI-powered enterprise document automation, especially transforming unstructured financial documents into structured outputs. Stands out for treating LLMs and agents as components within robust production systems, with emphasis on validation, security, observability, and scalable multi-agent architecture.”

“Engineering leader with 8+ years across IoT, emergency messaging, and political campaign data science, including operating a massive IoT microservices platform at several million devices and roughly 200 million requests per minute. Particularly strong in retrofitting legacy systems for modern observability using OpenTelemetry, Honeycomb, and Grafana LGTM, while also managing distributed international teams and developing engineers into senior and tech lead roles.”

“Frontend engineer with strong ownership in regulated healthcare/patient-facing products, including redesigning legal/privacy document workflows so non-engineering teams could safely publish updates without release cycles. Has also built frontend architecture from scratch, optimized large internal data-grid experiences, and recently worked in a forward deploy role directly with clients to shape product solutions.”

“Full-stack engineer with experience at Wipro and Cisco building real-time monitoring and observability platforms from scratch. Stands out for combining streaming/data-pipeline depth (Kafka, Fluentd, Elasticsearch, Redis) with strong React/TypeScript frontend execution, and for delivering measurable impact such as cutting troubleshooting time by nearly a third.”

Janvitha Mandyam

Screened

Mid-level AI/ML Engineer specializing in Generative AI and NLP

Chicago, IL4y exp

“GenAI/LLMOps practitioner who deployed a production RAG-based customer service and knowledge retrieval system for a global bank using LangChain, FAISS/Azure Cognitive Search, GPT-4/Claude, and Guardrails—driving a reported 35% Q&A accuracy lift while reducing handle time and escalations. Also partnered with non-technical leaders at CVS Health to deliver ML-driven supply chain risk and inventory insights via anomaly detection, NLG summaries, and stakeholder-friendly dashboards.”

“Backend/data engineer with hands-on production experience building reliable Python FastAPI services on Kubernetes and delivering AWS EKS + Terraform CI/CD with strong secrets isolation and rollback practices. Also built AWS Glue ETL pipelines into S3/Redshift with schema-evolution handling and data-quality controls, modernized legacy analytics into modular Python services with parallel-run parity validation, and has demonstrated SQL tuning impact (minutes to seconds) plus ownership of batch pipeline incidents end-to-end.”

Andrew Sheehan

Screened

Executive engineering leader specializing in enterprise architecture and digital transformation

27y exp

“Candidate has spent years working with startups across various industries and has been involved in multiple stages of company building, including hiring, technology choices, and budgeting. They appear especially oriented toward operational discipline, cost control, and evaluating ideas through structured cost, time, and value analysis.”

“Built and owned an end-to-end RAG-based AI support platform at Mechanize (FastAPI/LangChain/Pinecone/React) with rigorous evals and guardrails, driving 45% fewer support tickets and ~$280K annual savings. Also led a high-risk legacy modernization at Argo AI, incrementally extracting a monolithic Django backend using Strangler Fig + feature flags while supporting 10K+ concurrent users.”