Conversational Document
Retrieval System
Making large enterprise document repositories easier to explore through grounded, conversational search.
Explore case studyFull Stack GenAI Engineer / Kolkata, India
Making AI feel
like good engineering.
I build LLM-powered products that connect useful ideas to dependable systems—from conversational document retrieval to agents that make business data easier to reach.
Retrieval, reasoning, and the details that make them useful.
01 — A working modelInputs to insight
01A real question
02Relevant context
03A useful answer
Built withcare & context
01 The practice
Curious by nature.
Practical by design.
I’m a Full Stack Generative AI Engineer with experience building LLM-powered systems, RAG architectures, and AI-driven enterprise applications.
My work spans multi-model orchestration, vector search, conversational memory, and Python backends—alongside the responsive interfaces that make complex capabilities approachable. I care about useful context, clear system boundaries, and thoughtful implementation from end to end.
More about my experience02 Selected work
Systems with a
point of view.
A selection of AI engineering work, described through the problems, architecture, and implementation behind each system.
Conversational Document
Natural Language
to SQL Agent
Turning natural-language questions into schema-aware SQL with generation, validation, and refinement.
Explore case studyAutomated Quality
Assurance Agent
Helping quality teams review lab-report parameters against regulatory thresholds and spot discrepancies.
Explore case study03 Behind the systems
The thinking behind
the build.
Each project starts with a practical problem. Here’s how the pieces fit together.
01
Retrieval · Conversation · Context
Conversational Document
Retrieval System
The problem
Business users needed a more accessible way to find information across enterprise document repositories.
The architecture
Qdrant vector retrieval paired with embedding models via Azure OpenAI and Vertex AI, coordinated through a multi-step LangGraph pipeline with conversational memory.
The implementation
Designed chunking and retrieval, built Django/DRF backend APIs, and developed a responsive chat interface for real-time querying.
The outcome
Reduced document-search effort and improved information accessibility for business users; no quantitative result was specified.
02
Orchestration · Validation · Data access
Natural Language
to SQL Agent
The problem
Make relational business data easier to query without requiring every user to write SQL manually.
The architecture
A multi-LLM workflow using Azure OpenAI and Gemini through Vertex AI for query generation, validation, and refinement.
The implementation
Added schema-aware prompting and structured output parsing, then connected query execution to a results dashboard with conversational follow-ups.
The outcome
Accelerated business data access and reduced dependence on manual SQL querying; no quantitative result was specified.
03
Document intelligence · Rules · Reporting
Automated Quality
Assurance Agent
The problem
Quality teams needed to check lab-report values against FSSAI regulatory thresholds and identify discrepancies.
The architecture
An AI-driven document-intelligence workflow combining LLM-based structured parameter extraction with rule-based validation.
The implementation
Built parameter extraction, threshold comparison, discrepancy detection, and reporting workflows for QA teams and vendors.
The outcome
Reduced manual compliance-verification effort while improving accuracy and turnaround time; no numeric measurements were provided.
04 Experience
Good work is
built over time.
A path through full-stack development, applied AI, and systems designed to make everyday work a little easier.
Aug 2024 — Present
IT Executive · Full Stack GenAI Engineer
ITC Infotech · Kolkata, India
Building LLM-powered systems, conversational retrieval, natural-language data agents, and document-intelligence workflows. Selected work is detailed above.
Jul 2023 — Jul 2024
Junior Python Developer
AM Infoweb Pvt. Ltd. · Pune, India
Developed Python applications for medical-record management and LLM-assisted document workflows, including chronological organization, redaction, and automated delivery. Also implemented Selenium-based workflow automation.
05 Technical expertise
Tools for the
whole journey.
From a model call to the application around it, I enjoy bringing the full system into focus.
Generative AI & retrieval
LangChain, LangGraph, Google ADK, Azure OpenAI, Vertex AI, HuggingFace, OpenRouter, Ollama, LiteLLM, RAG, Qdrant, BM25, ColBERT, ColPali, hybrid retrieval, embeddings, metadata filtering, MCP, conversational memory, HITL workflows, guardrails, structured outputs, response evaluation.
Backend & data
Python, Django, Django REST Framework, FastAPI, REST APIs, PostgreSQL, MySQL, MongoDB, Redis, Apache Kafka, asynchronous processing, multiprocessing, multithreading, schema design, query optimization, unit testing.
Frontend & product interfaces
HTML, CSS, JavaScript, Bootstrap, jQuery, Streamlit, responsive interfaces, AJAX/fetch, dynamic rendering, data visualization integration, pagination, lazy loading, Selenium UI automation.
Platforms & delivery
Microsoft Azure OpenAI, Google Cloud Platform, Vertex AI, Docker, Git, GitHub, GitLab, service-account-based cloud integration.
06 Learning & foundations
Grounded in
curiosity.
Education
Maulana Abul Kalam Azad University of Technology
University of Calcutta
Courses & certifications
Certificate · March 2026
Certificate · April 2026
Certificate · October 2026
The next good question
Let’s make
something useful.
Interested in thoughtful GenAI engineering and practical product work? I’d be glad to connect.
✉️ Contact details
Email rakesh.shaw018@gmail.com
💼 LinkedIn https://www.linkedin.com/in/dev-rakeshshaw/
💻 GitHub https://github.com/dev-rakeshshaw
Thoughtful systems, made with care.Back to top