AI Engineer | AI Agent & RAG Developer | Python & Node.js Full-Stack Engineer | Freelancer
AI Engineer
Building Practical AI Solutions
AI Agents, RAG Systems, Automation, SaaS and Custom Software built for real-world business needs.
About
Building AI That Solves Real Business Problems
I'm Ajay Kumar Sharma, an AI Engineer and Freelancer specializing in AI Agents, RAG systems and full-stack software development.
I build production-ready AI solutions that connect intelligent models with real business data, tools and workflows. My work includes AI Agents, hybrid-retrieval RAG systems, AI automation, SaaS products, custom software and modern web applications.
I work remotely with businesses, startups and individuals worldwide, helping turn ideas, repetitive processes and complex requirements into practical software and AI solutions.
Core stack
Services
AI agent, automation and RAG development services
Six ways to work together. Every engagement is scoped and quoted before any build starts.
AI Agents & RAG Systems
Build intelligent AI Agents and RAG systems that work with your business data, tools and workflows.
ServiceAI Automation
Automate repetitive processes, workflows and business operations using AI and modern automation technologies.
ServiceCustom Software Development
Build tailored software solutions designed around your specific business requirements.
ServiceWeb App Development
Modern, responsive and high-performance web applications, built end to end.
ServiceSaaS Development
Turn your idea into a scalable SaaS product - from MVP to production.
ServiceAI & IT Consulting
Technical guidance, architecture and implementation support for AI and software projects.
RAG, stage by stage
How I actually build enterprise RAG
This is the pipeline behind every retrieval system I ship. Scroll it once and you will be able to tell whether anyone else's proposal is production-grade or a demo.
Your documents, wherever they already live
Nothing gets migrated. Standard connectors read SharePoint, Confluence, Google Drive, Notion, S3 and most SQL or NoSQL databases in place, and anything unusual gets its own connector.
- ◇One source for a pilot, three to five for a full platform build
- ◇Permissions read at ingest, not bolted on afterwards
Chunking that respects how the document was written
Most failing RAG systems fail here. Splitting on character count shreds tables, headings and clause numbering, and no amount of prompt tuning recovers meaning that was destroyed on the way in.
- ◇Structure-aware splitting, chosen per document type
- ◇Headings, tables and lists survive the trip
Embeddings, plus the metadata you will actually filter on
Vectors alone cannot answer "the 2024 policy for the EU entity". Metadata is extracted and indexed alongside the embedding, so retrieval can filter before it ranks.
- ◇Embedding pipeline tuned to your corpus, not a default
- ◇Metadata filtering and multi-index search across business units
Hybrid retrieval, never pure vector
BM25 + dense vectors + reranking, tuned to your corpus. Pure vector search ships demo-grade answers, not production ones.
- ◇BM25 catches exact terms, part numbers and clause IDs
- ◇Dense vectors catch the meaning that keywords miss
Reranking, with permissions applied before anything is shown
A reranker reorders candidates against the real question, and role-based access control drops anything the person asking is not cleared to see. This is where good retrieval becomes production retrieval.
- ◇Role-based access control and audit logs
- ◇Dynamic, permission-aware retrieval
Citations on every answer - and "I don’t know" is a valid one
Every response links back to the exact document and section. "I don’t know" is a valid answer. Hallucinations are a bug, not a quirk.
- ◇Every response links to the exact document and section
- ◇An evaluation harness, so your team scores accuracy over time
- ◇Monitoring, logging, alerting and runbooks your on-call can operate
My own products
Built, launched, and running
Four products I designed and built myself. Every one is live - open them and see.
NirixAI ↗
An AI learning assistant that turns YouTube videos, documents, and study materials into instant summaries, quizzes, and clear cited answers - so students master content without rewatching lectures or rereading pages. Built as a production RAG system on Pinecone with hybrid search, multi-LLM routing, and real-time streaming.
Serves streaming answers from a 20K+ content corpus with sub-2-second first-token latency.
PiperQL ↗
An open-source AI tool that lets anyone query a database in plain English instead of SQL - ops, finance, and HR teams ask a question and get the data plus the right chart, no analyst needed. Built as a LangGraph + OpenAI agent that turns natural language into SQL and holds conversation context.
Open-sourced under AGPL v3. Architecture scales to enterprise-grade query workloads.
Tim Edge ↗
A trading platform for testing strategies and fixing bad habits: backtest on four years of broker-exact data, auto-journal every trade with its emotional context, and execute one-click through your own broker. Built full-stack with MetaAPI + Fyers integration, WebSocket streaming, Celery workers, and AWS.
Live execution through cTrader, IC Markets and Fyers on a 3-worker Celery architecture.
Tim Academy ↗
A trading-education program teaching rule-based ("mechanical") systems through self-paced courses, live monthly execution labs, and a performance-based certification. Built full-stack with live streaming, auth, and payments on Firebase.
Six proprietary systems delivered with 100ms live streaming and a certification track.
Client work
Systems I built for other people
Delivered for client teams and running in production. Brands stay unnamed; the architecture does not.
Enterprise RAG pipeline architecture
Retrieval Augmented Generation pipelines with hybrid search, optimised embedding pipelines, context-aware retrieval, and metadata-driven search, built for international brand workloads.
Multi-index vector search system
An AI agent that searches several Pinecone indexes at once, with a universal metadata parser that normalises product fields across diverse catalogs - the pattern you need when retrieval has to span business units with different schemas.
AI assistant platform
Production AI assistants for several international brands - product recommendations, multilingual support, appointment booking, complaint handling, and FAQ automation, using fine-tuned models with ReAct reasoning chains.
Tech stack
What I build with
Stack-agnostic by principle - I pick tools to fit your problem, not a fixed toolkit. This is what I reach for most, and what the products above are actually running on.
AI engineering
Models
Backend
Data
Infrastructure
Frontend
How I work
Six standards I hold every build to
Whether it is a two-week audit or a four-month platform build, these do not move.
Architecture-first over prompt hacking
Every engagement starts with a retrieval architecture, not a prompt. Prompts are the last mile, not the first one.
Hybrid retrieval, never pure vector
BM25 + dense vectors + reranking, tuned to your corpus. Pure vector search ships demo-grade answers, not production ones.
Citations on every answer
Every response links back to the exact document and section. "I don’t know" is a valid answer. Hallucinations are a bug, not a quirk.
Production infrastructure, not notebooks
Monitoring, logging, alerting, auto-scaling, audit trails, and runbooks. Every deliverable is something your on-call can operate.
Your data stays in your cloud
Self-hosted, enterprise-API with zero data retention, or hybrid - chosen during the audit based on your compliance posture.
Handover, not dependency
Every engagement ends with your team able to operate and extend the system without me. No seat-licensing, no vendor lock.
Next step
Ready to build what you have in mind?
Bring your idea or a stuck project. We'll scope the architecture, map the build, and set a concrete plan. No sales pitch - just honest, actionable direction, straight from the person who would build it.