Vetted Data Structures Professionals

“Frontend engineer who built the full frontend for gameofstreaks.com using React/Next.js with an emphasis on responsive design, accessibility, SEO, and performance profiling. Has experience refactoring legacy HTML/CSS/JS sites into reusable React components and shipping quickly with unit tests, CI/CD, and branch-based deployments; also collaborates with designers in hackathon settings (e.g., an AI interview practice app).”

“Full-stack product engineer who owned a live-events digital ticketing platform end-to-end, including blockchain-based ticket validation and high-traffic booking flows. Stands out for combining Angular/React frontend work with Java/Spring Boot backend architecture, plus strong production reliability practices around concurrency control, queues, observability, and UX optimization.”

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).”

“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.”

“Built and deployed an AI Copilot for Healthful Telehealth that helps dietitians generate personalized meal plans using patient data and real-time clinical context. Stands out for owning the full lifecycle—from workflow discovery and ETL/RAG architecture to production incident response and post-launch stabilization—while delivering roughly 30% gains in retrieval accuracy and latency.”

“Internship experience building an end-to-end medical AI pipeline that extracts and normalizes messy medical PDFs, fine-tunes BioBERT to classify tumor-related statements (including negation/ambiguity handling), and integrates image-model outputs (MedSAM/GroundingDINO) for tumor localization and classification. Also worked on an LLM/RAG system to draft IPO prospectuses using retrieved regulatory/financial sources (including SEC EDGAR) with structured prompts to reduce hallucinations.”

“Backend/ML engineer who has shipped high-scale real-time systems across e-commerce and healthcare: built a PharmEasy real-time recommendation engine for ~2M monthly users (cut feature latency 5 min→30 sec; +15% cross-sell) and architected a HIPAA-compliant multimodal clinical diagnostic workflow (DICOM+EHR) with XAI, MLOps (MLflow/Airflow/K8s), and drift/monitoring guardrails supporting 10k+ daily predictions.”

Manasa Pantra

Screened ReferencesStrong rec.

Junior Software Engineer specializing in AI, LLM systems, and full-stack development

Stony Brook, NY2y exp

“Product-focused full-stack engineer at startup (Zippy) who shipped a production multi-agent AI system for restaurant operations plus payments workflows. Built end-to-end: RAG grounded on a Notion knowledge base, structured function-calling task routing, FastAPI/JWT multi-tenant backend, and a polished React+TypeScript owner dashboard. Has real production incident experience (duplicate Stripe webhooks) and reports ~94% task-routing accuracy under load.”

“AI/ML infrastructure-focused engineer who has built production RAG systems from scratch (Supabase/pgvector + OpenAI embeddings) and iterated using formal eval metrics to improve retrieval quality. Also debugged real-time audio issues in a LiveKit-based pipeline by correlating packet loss with VAD behavior, and has deep experience building brittle, customer-specific financial platform integrations in Python/Playwright (2FA, redirects, token refresh, rate limits).”

“Backend engineer at Cerebrone.ai building cloud-native Flask microservices for an AI-driven automation platform on GCP (Cloud Run/App Engine), including dedicated inference services integrating OpenAI and internal ML pipelines. Demonstrated strong performance and scalability wins across Postgres/SQLAlchemy optimization, multi-tenant (healthcare/HIPAA-grade) data isolation, and high-throughput background processing with Celery/Redis/RabbitMQ, with multiple quantified latency/CPU/throughput improvements.”

“Cofounded a travel app and built a production place recommendation + review system end-to-end using Next.js App Router and TypeScript, including Postgres-backed APIs and post-launch monitoring. Uses structured logging with Sentry and Vercel Analytics to diagnose issues and validate performance improvements, and has some exposure to Temporal-based workflow orchestration with retries/idempotency.”

Priyanshi Sharma

Screened

Mid-level Software Engineer specializing in FinTech full-stack and backend systems

Parsippany, NJ4y exp

“Built and productionized a GenAI prompt-engineering solution to retrieve prevailing wages based on job/location selections, emphasizing accuracy through stricter prompt templates and validation. Hands-on in real-time production debugging using Splunk (callback tracing, verbose logging, header inspection) and experienced running developer-facing demos/workshops that helped drive marketplace API adoption.”