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Vetted LLM Integration Professionals
Pre-screened and vetted.
Principal Full-Stack Engineer specializing in FinTech, Healthcare IT, and AI integrations
Staff Software Engineer specializing in full-stack SaaS, CMS platforms, and AI systems
Senior Frontend Engineer specializing in React, Vue, and AI-driven UI
Director-level AI & Software Architect specializing in privacy-first LLM/RAG SaaS
Mid-Level Software Engineer specializing in distributed systems and serverless platforms
Senior Full-Stack & AI Engineer specializing in Python, React, and LLM/RAG systems
Junior Full-Stack Software Engineer specializing in cloud-native healthcare applications
Director-level Senior Software Engineer specializing in full-stack, mobile, and EdTech/AI platforms
Senior Software Engineer specializing in Python backend, microservices, and cloud/DevOps
Senior Frontend Developer specializing in React/Next.js for interactive streaming platforms
Senior Full-Stack Developer specializing in MERN, cloud platforms, and LLM-powered applications
Senior Full-Stack Software Engineer specializing in AI/LLM-powered web applications
Tirth Shah
Mid-level AI/ML Engineer specializing in anomaly detection, data tooling, and cloud-native systems
“Backend/platform engineer who built an LLM-driven QA automation system (“mockmouse”) using a Flask orchestration microservice, Socket.IO real-time updates, Redis caching, and strict Pydantic schemas to turn prompts into reliable action graphs and automated browser tests. Has hands-on Kubernetes delivery experience (Docker/Helm/Jenkins) and has supported large migration programs, validating ETL cutovers with 1M+ synthetic records and rigorous output comparisons; also built event-driven monitoring/anomaly detection streaming into Grafana.”
Vaibhav Dabhi
Mid-Level Full-Stack Software Engineer specializing in Java microservices and cloud-native delivery
“Built and shipped a production LLM feature that explains DSA problems with real-life explanations, using Grok with automatic failover to OpenRouter (and multiple backup models) to avoid user-facing failures. Improved cost efficiency by implementing difficulty-based token budgets and iterated prompt quality via structured constraints and an in-app feedback mechanism, reporting satisfaction across 38 users.”
HarshelSrivatsava Srivatsava
Intern Full-Stack Engineer specializing in AI-powered SaaS products
“Solo builder of OGym, shipping production AI features for gyms that turn member behavior/health data (workouts, attendance, nutrition, payments, device metrics) into prioritized, actionable owner and member insights. Designed and implemented FastAPI backends, PostgreSQL-based RAG workflows, guardrails (RBAC/validation/rate limiting), and real-user evaluation loops, with a strong focus on latency/cost optimization and reliable data pipelines.”
Dhwani Patel
Mid-level Full-Stack Python Developer specializing in AI/ML and backend APIs
“Python/Django backend engineer with open-source experience upgrading Archivematica to Django 4.2 LTS, including resolving a tricky breaking change in datetime parsing by implementing a preservation-safe legacy timestamp conversion layer. Also built a cost-efficient, reproducible Small Language Model (Microsoft Phi-3) fine-tuning pipeline that turns CSV product data into a domain-specific searchable Q&A chatbot, with emphasis on memory optimization and overfitting prevention.”
Kaushik Naik Guguloth
Entry-level Software Engineer specializing in AI/ML, cybersecurity, and full-stack development
“Built end-to-end product features for a Web3 monetization platform and also shipped a privacy-first mobile accessibility app, SenScribe, using on-device sound classification and LLM summarization with zero cloud dependency. Particularly interesting for roles spanning full-stack product engineering, mobile AI, and applied ML where careful debugging, stakeholder alignment, and real-world usability matter.”