AI Engineer | Data Scientist | Builder of LLM Systems, Multi-Agent Frameworks, and Data Platforms

I design and build production AI systems that convert fragmented, multi-source data into intelligence, automation, and decision-ready insights.

My work focuses on building practical AI infrastructure at the intersection of:

• LLM systems and agent architectures
• web-scale data extraction
• retrieval and enrichment pipelines
• scalable AI infrastructure

I focus on systems that run reliably in production — not experimental prototypes.

Designing AI systems that combine reasoning, retrieval, and automation.

Key capabilities:

• multi-agent reasoning architectures
• LLM-driven extraction pipelines
• retrieval-augmented knowledge systems
• automated decision workflows

Engineering pipelines that convert complex web environments into structured knowledge.

Areas of focus:

• Playwright and Selenium scraping infrastructure
• dynamic JavaScript extraction and anti-bot handling
• entity resolution and enrichment systems
• automated research intelligence platforms

Building reliable AI infrastructure and production data pipelines.

Typical architecture components:

• FastAPI microservices
• queue-driven pipelines and retry systems
• distributed enrichment engines
• validation and failover layers

I maintain multiple Python libraries on PyPI focused on AI infrastructure, agent systems, and data automation.

PyPI profile
https://pypi.org/user/irfanalidv

Selected projects:

A production-grade framework for multi-agent AI orchestration.

Key capabilities:

• ReAct agents
• swarm and debate reasoning
• router and planner architectures
• workflow graphs
• observability and cost tracking

Comparable to systems such as LangGraph, CrewAI, and AutoGen.

A framework designed to improve reliability in RAG systems.

Features:

• automated query variation generation
• retrieval confidence scoring
• fallback and retry strategies
• cost tracking and metrics

Hybrid retrieval architecture combining:

• BM25 search
• vector embeddings
• structure-aware graph expansion

Designed to improve retrieval accuracy in knowledge systems.

A workflow engine for building large-scale scraping pipelines using Playwright.

Kuration AI (Hong Kong — Remote)

Built the intelligence infrastructure powering:

• universal scraping systems across 50+ global sources
• multi-API enrichment engines with waterfall routing
• LLM-based classification and extraction pipelines
• production FastAPI services for real-time intelligence

Technology stack

Python
Playwright
FastAPI
LangChain
MongoDB
LLM APIs

Luminous Power Technologies

• Built organization-wide analytics and BI platforms
• Defined enterprise data strategy
• Implemented ML experimentation environments

Data Analytics and Automation — Lynk
Head of Data and Analytics — Brainsfeed
Data Scientist — RightCust Technologies
Developer Evangelist — DevMetric
Data Visualization Developer — DatavisTech (San Francisco)

Languages
Python
SQL
R

Machine Learning and AI
LangChain
LLM APIs
scikit-learn
NLP pipelines

Data Engineering
FastAPI
PostgreSQL
MongoDB
REST APIs

Web Data Extraction
Playwright
Selenium
Scrapy

Cloud and DevOps
Azure
GCP
Docker
GitHub Actions

Analytics and Visualization
Jupyter
Power BI
RStudio

M.Sc. Data Science and Artificial Intelligence
Indian Institute of Science Education and Research (IISER), Tirupati

B.Tech Computer Science and Engineering
Alliance University

International Exchange Program
ISEP Paris

Winner — Philips Digital Healthcare Conclave

Maintainer of multiple Python libraries on PyPI focused on AI infrastructure and data systems.

Built AI intelligence platforms integrating more than 100 data sources.

Published research in AI and neural-symbolic NLP.

LinkedIn
https://www.linkedin.com/in/irfanalidv

GitHub
https://github.com/irfanalidv

I am interested in collaborating on AI infrastructure, multi-agent systems, retrieval pipelines, data intelligence platforms, and open-source AI tooling.