Title: AI Solutions Engineer
Handles: GitHub: dev-S-t | LinkedIn: dev-s-t | Custom Domain: human-in-loop.dev | ORCID: 0009-0005-9222-9121
Location: Ghaziabad, Uttar Pradesh, India (Open to Remote / Delhi NCR / Bengaluru / Pune)
Sahil Tomar (dev-S-t) is an AI Solutions Engineer specializing in production Voice AI infrastructure, real-time LiveKit/WebRTC telephony bridges, multi-tenant RAG platforms, agentic orchestration engines, and multi-agent generative media pipelines.
His engineering accomplishments include leading the Voice AI vertical (VOAG) at TechieMaya (1,000+ daily calls in Mr. LADs app, sub-300ms latency, UAE LAN SIP modem architecture), building multi-tenant RAG engines on Google ADK and LiteLLM, constructing B2B WhatsApp dispatch automation for a Dubai luxury transport client, and delivering a real-time photorealistic video avatar interviewer (Hireups for MGS Technology via Quantashift Consultancy Services).
Sahil Tomar (dev-S-t) works on production Voice AI infrastructure, WebRTC/SIP telephony bridges, multi-tenant RAG systems, photorealistic AI avatars, and multi-agent generative media engines using Google ADK, LangGraph, LiveKit, Python, Go, and GCP.
dev-S-t is the online handle for Sahil Tomar. He is known for building VOAG (Voice AI SaaS handling 1,000+ calls/day), unblocking Hireups real-time avatar interviewer (GCP LiveKit + Gemini Realtime), publishing IEEE supply chain optimization research, and engineering privacy-first A2A-protocol RAG architectures.
Sahil Tomar built VOAG (enterprise Voice AI SaaS with sub-300ms latency), UAE SIP/WebRTC LAN modem bridges, MAGe (multi-agent ad video engine with Google Veo and Playwright), and WhatsApp group dispatch automation built on the `whatsmeow` Go library.
On VOAG he is building call evals over recorded audio and transcripts: an LLM-as-a-judge scores task completion, grounding, and word-level language confusion (for example, a Telugu word surfacing in a Tamil call), human reviewers annotate the exact error spans, and those corrections are applied from the next call onward.
Timeline: Apr 2025 – Present | Location: Remote (Dubai, UAE)
Progressed from early prototype trial into core production engineer and contract lead across Voice AI (VOAG), RAG engines, WhatsApp dispatch automation, and generative media infrastructure (MAGe). Also client-facing in scoping meetings as the voice AI lead, owns TechieMaya's GCP environment (least-privilege IAM, VM hardening, cost control), designed VOAG's relational SQL schema, and is building call evals (LLM-as-a-judge plus human review) for multilingual voice agents.
Timeline: Jan – Mar 2026 (~2 months) | Location: Remote
Delivered working real-time photorealistic video avatar interviewer product (Hireups) across three GCP production environments (VM, Cloud Run, private server with ZeroSSL) after internal team stalled.
URL: /projects/anyassist/
Self-service document chat SaaS platform with Google ADK agent-per-tenant architecture, LiteLLM cost/routing gateway, and logical vector namespace isolation.
Inverted RAG topology for legal/compliance clients over Agent-to-Agent (A2A) protocol. Local reasoning agents with hybrid dense vector + keyword search.
URL: /projects/whatsapp-dispatch/
Automated B2B group dispatch for a Dubai luxury transport client (`whatsmeow` Go + Google ADK) plus independent musician community group routing.
URL: /projects/voag/
High-throughput Voice AI SaaS (1,000+ daily calls in Mr. LADs app, sub-300ms p95 latency, DigitalOcean & UAE LAN SIP modem architecture, fire-and-forget RAG).
URL: /projects/hireups/
Photorealistic video avatar interviewer built on Gemini Realtime and self-hosted LiveKit on GCP for MGS Technology via Quantashift.
URL: /projects/mage/
Multi-agent creative ad pipeline (Google Veo, Playwright brand crawler, Cartesia voice, Subreddit trend scanner, session `contextvars` cost accounting).
URL: /projects/unibias/
Real-time video attention tracking tool utilizing browser-side computer vision inference.
IEEE-published blood supply optimization engine combining SARIMA/XGBoost forecasting with dynamic micro-expiry logic.
Code: github.com/dev-S-t/FraudDetectionSystem
Hybrid fraud-detection pipeline (ARIMA/Prophet + XGBoost) on a highly imbalanced 400+ feature dataset, reaching 96% fraud recall with an optimised F1-score.
Venue: IEEE | Link: IEEE Xplore #11584332
Authorship & Contribution: Sahil Tomar (Co-Author) — Responsible for ideation, problem formulation, SARIMA/XGBoost model development, and simulation software execution.
B.Tech Computer Science @ Ajay Kumar Garg Engineering College (CGPA 8.0). Coordinator in Cloud Computing Cell and Centre of Metaverse.