Python Developer • AI/ML Engineer • Jaipur, Rajasthan
Tanu Meena
"Engineering resilient agentic workflows and high-precision computer vision architectures with uncompromising technical rigor."
Graduating in Artificial Intelligence and Data Science from MBM University (CGPA 7.61), I bridge deterministic software orchestration and advanced neural computation. My core expertise spans stateful multi-node workflows using LangGraph and LangChain, model-driven retail analytics with YOLO and ResNet18 at Tata Consumer Products, and clinical risk evaluation models for DRDO.
Dedicated to architecting production-ready systems that optimize resource efficiency, enforce strict state boundaries, and deliver reliable decision-making intelligence at scale.
Specialized Technical Verticals
Agentic Orchestration & LangGraph
Designing stateful LLM workflows using LangGraph and LangChain. Specialized in sequential, conditional, looping, and multi-input task execution with Model Context Protocol (MCP) for secure, tool-connected reasoning engines.
Computer Vision Pipeline
Two-stage detection and similarity pipeline using YOLO and ResNet18 for SKU matching and planogram analysis, with documented evaluation limits and failure modes.
Clinical ML
ROC-AUC optimized heart failure risk prediction models using structured clinical datasets.
Deployment & APIs
FastAPI & Flask model serialization, OpenCV preprocessing, and Git workflows.
Technical Deployments & Architecture
Explore System Case Studies & GitHub Repositories
LangGraph Agent Workflows
Stateful multi-step AI task execution
Problem
Complex LLM applications require deterministic state management, cyclic execution, and secure tool orchestration over multi-step reasoning.
Approach
Engineered graph-based reasoning workflows employing sequential, conditional, and looping logic with explicit state boundaries to guarantee reliable context handoffs.
Technology Stack
Interactive Architecture
Select nodes to inspect pipelineState Initialization
Defines the explicit typing and schema for the graph payload, ensuring strict data boundaries before multi-step execution begins.
Shelf Product Identifier
Automated SKU planogram compliance
Problem
Manual retail shelf analysis is inefficient and error-prone, requiring an automated system capable of recognizing diverse SKUs from unstructured images.
Approach
Designed a robust two-stage pipeline separating product localization (YOLO) from specific category classification (ResNet18) to improve overall precision.
Result
85 detections
Saved sample run
Technology Stack
Interactive Architecture
Select nodes to inspect pipelineImage Ingestion
Utilizes OpenCV and NumPy arrays to structurally load and preprocess unformatted retail shelf imagery.
Heart Failure Prediction
Clinical risk evaluation model
Problem
Predicting heart failure risk requires accurate processing and evaluation of structured clinical datasets to assist in medical decision-making.
Approach
Developed a machine learning pipeline evaluated with strict classification metrics and ROC-AUC optimization on clinical data.
Technology Stack
Interactive Architecture
Select nodes to inspect pipelineClinical Data
Ingestion and structured preprocessing of raw clinical datasets containing patient health indicators.
Professional Experience
AI/ML Intern • SOSEO TECH Advisory (Delhi)
Developed and tested Python-based AI agent workflows using LangGraph and LangChain for stateful, multi-step task execution. Implemented sequential, conditional, looping, and multi-input workflows with explicit state transitions.
ML Intern • Tata Consumer Products Limited (Bengaluru)
Developed an end-to-end Python computer-vision pipeline using YOLO and ResNet18 for automated retail shelf-image analysis, with evaluation boundaries documented alongside the project.
AI-ML Intern • DRDO (Jodhpur)
Developed a Python machine learning pipeline for heart-failure risk prediction using structured clinical data, evaluated with classification metrics and ROC-AUC.
Expeditions & Co-curricular
Travel Expeditions
Karnataka & Tamil Nadu
Coorg, Chikmagalur, Mysuru, Bengaluru, Ooty, Coonoor
Uttarakhand
Rishikesh, Ukhimath, Mussoorie, Rudraprayag, Tungnath, Chandrashila Peak
Rajasthan & NCR
Jaipur, Jodhpur, Udaipur, Delhi
Hobbies & Versatility
Passionate about trekking, exploring, hiking, and listening to music. Equipped with diverse skills outside of code.
Volunteer Work
- Technical Team Volunteer
- Marketing & Designing Team
- City Youth Program
2021 Award
Awarded a laptop from Super 30 coaching for JEE by IOCL, CSRL (Jaipur).