AI Hiring Platform
MarqHire AI
Hiring platform that parses resumes, scores them against a job description, and runs first-round screening interviews by voice.
Screening automated end to end
Voice AIATSReactNLP
Dixit Jain, AI Engineer & Automation Consultant. Most AI stops at the demo. I build the part that lasts.
LLM agents, retrieval systems, voice AI, and the automation around them. 25+ shipped to production, serving users in 10+ countries.
25+
Systems in production
85%+
RAG retrieval accuracy
12+
Teams worked with
40%+
Manual work removed
50+
Workflows automated
10+
Countries with live users
Built for teams shipping real products
Plus finance and government engagements under NDA
About
Systems that survivereal users.
Not a portfolio of demos. A record of AI systems designed, shipped, and still running in production.
01
Demos are easy. Production is the job.
Most AI projects die between the notebook and the deploy. I build the part that survives: evaluation, fallbacks, observability, and robust systems that behave when real users show up.
02
Business problem first, model second.
Every system I ship starts from an operational pain: leads rotting in an inbox, support queues on fire, knowledge trapped in PDFs. I work backwards to the smallest architecture that removes it.
03
Full stack, end to end.
Agent orchestration in LangGraph, retrieval on hybrid search, FastAPI backends, and React frontends. One person owns the whole system, so nothing gets lost in the handoff.
How I build
The same loop for every system, whether it's a job or something I ship on my own.
- 01
Understand
The real problem and its constraints, before touching a model or a framework. Output: a clear picture of what production actually requires.
- 02
Design
System architecture, model choices, and data flow, thought through and written down before any code.
- 03
Build
Production-focused implementation in short iterations, tested early against real inputs, not synthetic ones.
- 04
Ship
Deployed, monitored, and documented. No black boxes and no handoff gaps.
- 05
Improve
Evaluated and tuned after launch. Systems get better in production, not worse.
Experience
Enterprise exposure,to product, to AI.
Five roles, each a different part of the same progression: understanding how enterprise systems get built, learning to think in products, then engineering the AI systems themselves.
Builds production-grade Generative AI and Agentic AI systems for enterprise use, with the emphasis on systems that stay reliable under real load rather than experimental prototypes.
- Builds production Agentic AI and LLM applications with LangGraph and FastAPI
- Designs RAG architectures on LlamaIndex and Qdrant with hybrid search and grounded generation
- Engineers scalable AI agents on Python, Azure AI, and Redis with stateful workflow orchestration
- Implements AI safety and reliability mechanisms: PII masking, content safety, and jailbreak detection
- Improves production systems through observability and parallel execution
LangGraphFastAPILlamaIndexQdrantAzure AIRedisPython
Selected Work
Systems built for scale.
Systems in production. Real products with real users, not weekend demos.
Travel Booking Platform
Fly with Zara
Full-stack travel booking platform for flights, hotels, holidays, and visa services.
Thousands of bookings processed
ReactNode.jsFull StackBooking API
AI Website Builder for Small Businesses
Brandry
Describe your business in one sentence - Brandry builds a real, mobile-ready website instantly. No code, no designers, no jargon.
One prompt to a live site
Next.jsOpenAITypeScriptAI Generation
Multi-Agent Document Intelligence
AgentFlow
Multi-agent platform that turns complex business files into cited, verifiable executive briefs.
85% reduction in document review time
LangGraphMulti-AgentRAGFastAPI
Self-Hostable Open-Source Second Brain
OpenBrain
Capture reels, posts, links, and screenshots you save. Openbrain reads, summarizes, and lets you find them in plain language.
Plain-language search over everything saved
Next.jsVector DBLLMOCR
AI Health & Wellness Platform
Wellnix
AI health platform combining motion analysis, real-time nutrition intelligence, and predictive wellness.
Posture scored from phone video
Computer VisionReactPythonAI Health
Case Studies
Selected work,in depth.
Three systems, each measured by what changed after launch. Open one for the full architecture and approach.
01 Sales Automation
AI Lead Pipeline for a Sales Team
A B2B services team was hand-sorting 200+ inbound leads a week, with reps spending more time on data entry and triage than selling.
n8nLangChainCRM Integration
80%
less manual sorting
3x
faster first response
02 Knowledge Retrieval
Production RAG for Enterprise Documents
An internal team lost hours every week digging through scattered PDFs, wikis, and ERPNext records to answer routine questions.
RAGPineconeLangGraph
85%+
retrieval accuracy
Hours
saved per team weekly
03 Customer Support
Voice AI for Customer Support
A growing support team was buried under repetitive Tier-1 inquiries, driving long wait times and agent burnout.
Voice AIWhisperVapi
60%
resolved without a human
24/7
availability
Under the hood
The tools, and the loopthey run in.
Every tool I ship with, connected the way the work connects, and the reasoning loop underneath every agentic system I build.
The map · drag a node to explore
drag nodes · hover to explore
The loop · every system runs this
User
Query, voice, or event
Agent
LangGraph orchestrator
Reasoning Core
LLM · tools · memory
Vector DB
Hybrid semantic retrieval
Response
Grounded and cited
Contact
Let'sconnect.
Everything above is the introduction. If it fits a role you're hiring for, or something you're building, write to me. Questions about how any of it works are just as welcome.