Vetted Role-Based Access Control (RBAC) Professionals

Yash Mahajan

Screened

Junior Software Engineer specializing in AI, full-stack development, and applied ML

Fullerton, CA2y exp

“AI/full-stack product builder who has shipped production agentic systems in both customer support analytics and medical claims automation. They combine React/Next.js frontends with Python-based async backends and LLM orchestration, delivering measurable outcomes like 60% cost savings, 40% less manual review, and reducing claims processing from 30 minutes to 20 seconds.”

“Full-stack and AI product engineer who built and deployed two end-to-end projects: TrekTale, a travel story management app, and PromptCraft Finance, an AI-powered financial assistant using multiple open-source LLMs. Particularly strong in turning ambiguous product ideas into modular, production-oriented systems with typed frontend/backend contracts, multi-model inference pipelines, structured outputs, and monitoring for latency, reliability, and regressions.”

Parth Shroff

Screened

Entry-level Full-Stack Software Developer specializing in AI and cloud applications

Remote, USA1y exp

“Full-stack engineer who has independently owned and shipped educational web products from concept through launch, including an AI-powered thermodynamics tutoring app and a physics learning platform tied to research publication. Strong in React plus AWS serverless architecture, with a clear pattern of working directly with non-technical stakeholders, iterating from user feedback, and maintaining production systems end-to-end.”

“IBM Power/AIX engineer who has owned a large production estate (20+ Power9/Power10 frames and 400+ LPARs) with vHMC and dual-VIOS HA. Has hands-on incident recovery experience (NPIV/RMC issues, LPM restores) and PowerHA failovers, plus modern DevOps exposure using Terraform on AWS and CI/CD with GitHub Actions/Jenkins (including deploying AI/RAG and vision workloads).”

“Backend/AI engineer who built and refactored a production RAG system over IRS Form 990 filings for 60 nonprofits, using a dual-path architecture (deterministic financial ranking + TF-IDF semantic retrieval) to keep latency sub-2s and reduce hallucinations. Demonstrates strong API craftsmanship in FastAPI (contract-first, OpenAPI-driven) plus production-grade security for multi-tenant systems (JWT, RBAC, Supabase-style RLS) and careful migration practices (feature flags, traffic mirroring, incremental rollout).”

“Full-stack engineer from an early-stage AI SaaS startup who owned and shipped a production AI-powered PDF document chat and sharing feature end-to-end (React/TS + Node + Postgres on AWS). Demonstrates strong product thinking through layered success metrics and tight feedback loops, plus hands-on reliability/observability work (CloudWatch, structured logging, alarms) and robust ingestion pipeline patterns (idempotency, retries, reconciliation).”

Haneesh Kapa

Screened

Junior AI Full-Stack Engineer specializing in LLM automations and RAG systems

Nashua, NH2y exp

“Built and shipped a production LLM-powered customer support assistant using a Python/FastAPI backend with RAG (embeddings + vector search) over internal docs and product/operational data. Instrumented the system with logging/metrics and ran continuous eval loops; post-launch improvements focused on retrieval quality (chunking/ranking) and performance/cost tradeoffs (query classification, caching, validation guardrails).”

Purav Patel

Screened

Entry-level Full-Stack Software Engineer specializing in .NET, React, and cloud systems

Houston, TX1y exp

“Full-stack developer with .NET and cloud-based development experience who has built a practical AI-assisted engineering workflow using Copilot, Claude, and GPT across coding, debugging, testing, and review. Stands out for using AI to accelerate delivery while still applying careful validation and human judgment for high-impact performance and security decisions.”

“Software engineer building AI-powered automation features in commercial real estate, including brochure generation and property listing workflows. They combine FastAPI/Redis/Celery backend architecture with multi-agent LLM design, structured prompting, testing, and production monitoring, and are now actively learning RAG and vector databases to make outputs more personalized.”

“Frontend-focused engineer who built the entire UI architecture for a GenAI product as the sole founding engineer, shipping a unified experience across iOS, Android, and web. Stands out for combining cross-platform Flutter expertise with browser-level performance tuning, accessibility, and design-system thinking, including measurable wins like 45% faster load times, 94% accessibility, and dramatically faster engineer onboarding.”

“Solo builder of ZenDSA, a live AI-powered DSA learning product with 37 real users, built end to end using Java/Spring Boot, React, and TypeScript. Particularly interesting for teams building AI products: they designed a production LLM fallback architecture, enforced structured JSON outputs, monitored parse-failure regressions, and fixed an SSRF vulnerability after launch.”

“Frontend-focused developer who built Butterfly Booking, a live two-sided salon scheduling platform for both business owners and customers. Demonstrated strong ownership across UI architecture, AWS deployment, responsive design, and a mid-project database migration, with results including a successful replacement of Vagaro, over $3,000 in annual savings for the client, and roughly 20% growth in bookings after launch.”

“Backend Python engineer who owned a real-time manufacturing insights streaming service, building FastAPI async microservices with Kafka-style queue buffering, batching/backpressure, and a low-latency snapshot store. Led a serverless-to-Kubernetes (EKS) migration at UGenomeAi using GitOps-style GitHub Actions pipelines, standardized config/secrets, and improved deployment consistency with pinned dependencies and multi-stage Docker builds.”

“Built and shipped production LLM-driven pipelines for clinical data processing, turning semi-structured inputs into validated structured outputs for downstream analytics. Emphasizes predictability and safety via strict JSON schemas, state-machine orchestration, backend-controlled tool calling, and robust fallbacks (rule-based checks/manual review) plus monitoring and offline/online evaluation loops; also has experience hardening workflows against messy ERP/finance data with idempotency and state tracking.”

“Full-stack product engineer who has built both a production-grade React/Next.js analytics platform and a Stripe-based subscription system end to end, with measurable business impact including churn reduction, reporting automation, and cost savings. Also stands out for 0-to-1 work in the digital art space, where they built an AI-powered cataloguing and delivery pipeline over 30TB of assets using computer vision, Shopify, and cloud infrastructure.”