Vetted Tailwind CSS Professionals

Rathi Anand

Screened ReferencesStrong rec.

Senior Full-Stack Software Engineer specializing in Insurance, FinTech, and AI/ML applications

Dublin, CA17y exp

“AI/backend engineer who fine-tuned and deployed a production LLM chatbot using a LangChain + FAISS RAG pipeline, improving latency with PEFT/LoRA and driving strong business impact (40% customer adoption; 92% satisfaction). Also served as technical lead on a data aggregation system for underwriting/quoting, introducing GraphQL for more efficient, maintainable querying and applying CDC to keep cached ranking data fresh at scale.”

“Product engineer/designer with founder mindset who shipped a blockchain-enabled investor group/governance platform using Next.js (App Router), TypeScript, Prisma/Postgres, and Temporal. Emphasizes auth-centric onboarding (SSO + embedded wallet) to make dApp UX feel more like SaaS, and brings strong reliability practices (idempotent retries, reconciliation) plus experience demoing to investors and operating in seed-stage teams (ex-Vouched).”

“Built a production AI-powered sales automation system that reads inbound product enquiry emails, extracts structured data, and routes decisions via a rules-based workflow integrated with a product database. Leverages Gemini structured outputs/schema plus option-based prompting and validation to keep responses reliable, and optimizes latency by breaking agent reasoning into smaller LLM calls; evaluates workflows with LangSmith and metrics like completion rate and accuracy.”

“Built and owned an enterprise AI document-processing deployment at an automotive tech startup, taking it from discovery to stabilization. Strong in production LLM/RAG systems and backend reliability, with measurable impact including 8,000+ documents processed monthly and turnaround time reduced from nearly 24 hours to about 3 hours.”

“Full-stack + ML engineer who built “Finsight,” a real-time financial risk platform (React/FastAPI/MongoDB/AWS Lambda) processing 2M+ records monthly, using sharding and Redis caching (60% DB load reduction) plus async and batch optimizations. Also has healthcare product experience at Apollo Healthcare, partnering directly with clinicians/admins to design and iterate EHR dashboards via Figma prototyping and user testing, and demonstrates clear system design thinking for real-time voice-to-LLM architectures.”

“Full-stack engineer from Vagaro who owned an end-to-end rebuild of a sluggish WordPress sales site into a Next.js 15 app hosted on Azure, adding Hygraph headless CMS, SSR, i18n, and a reusable component library. Instrumented Amplitude + A/B testing/heatmaps and reports a 10% sales lift post-launch; also has AWS ops experience (S3/IAM/CloudWatch) and has built ingestion pipelines including LLM-powered unstructured data processing with dead-letter handling.”

“Backend engineer who built and evolved the real-time networking/messaging backend for a cross-platform professional networking app (Make Connexions), optimizing for low-latency delivery, privacy, and strong consistency. Experienced scaling Python/FastAPI APIs with async + Redis, and leading safe refactors via versioned endpoints, feature flags, and backward-compatible migrations; strong production auth/RLS expertise including refresh-token rotation edge cases.”

“Backend/data engineer focused on production-grade Python services and AWS platforms: builds FastAPI microservices on EKS with strong reliability patterns, CI/CD, and observability. Also delivers AWS Glue/Redshift analytics pipelines with schema-evolution and data-quality safeguards, and has modernized legacy batch processing into maintainable services with parallel-run parity validation and feature-flagged rollouts.”

“Frontend engineer who has owned end-to-end delivery of multi-step commerce workflows, including an e-commerce cabinet customization platform and a food/grocery pickup ordering flow. Emphasizes scalable React architecture (Redux/RTK Query), strong automated quality (TDD, Jest/RTL, ESLint/Prettier/Husky), and performance/cost optimization via API caching and reduced AWS Lambda calls.”