Vaidansh Mali

CEO

Bengaluru, Karnataka, India1 yr 2 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Architected an AI framework reducing manual effort by 85%.
Achieved 85% accuracy in soil suitability predictions.
Top 10% in LeetCode contests and multiple hackathon finishes.

Stackforce AI infers this person is a Machine Learning and AI specialist in Industrial Automation.

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Skills

Core Skills

Artificial Intelligence (ai)Software DevelopmentMachine LearningData Analysis

Other Skills

AlgorithmsC++Convolutional Neural Networks (CNN)Data StructuresDatabasesDeep LearningElectronicsFeature EngineeringFront-End DevelopmentJavaScriptLarge Language Models (LLM)MERN StackMongoDBNode.jsOpenCV

About

A final-year Electronics and Instrumentation Engineering student at Manipal Institute of Technology, I am passionate about building intelligent systems that solve complex challenges at the intersection of industrial automation and AI. My experience is defined by delivering measurable results and architecting robust, end-to-end software solutions. During my R&D internship at Schneider Electric, I architected and developed an enterprise-grade, AI-powered framework to migrate industrial automation code. By leveraging multiple Large Language Model (LLM) APIs and engineering a migration engine that supports over 600 error types, my work reduced manual effort by 85% and achieved a 90% error-free code generation rate. Previously, as a Machine Learning Intern at MPRASS Mining Solutions, I developed and deployed ML models that analyzed drone geolocation data, achieving 85% accuracy in predicting soil suitability. My work on feature engineering and automating data preprocessing pipelines directly boosted the efficiency of identifying new mining sites by 20%. Beyond my internships, I continuously sharpen my problem-solving skills, evidenced by a Top 10% LeetCode contest rating and multiple Top 5 finishes in the Smart India Hackathon. I am now eager to bring my technical expertise in Machine Learning, system architecture, and full-stack development to a forward-thinking organization.

Experience

1 yr 2 mos

Total Experience

7 mos

Average Tenure

8 mos

Current Experience

Zs

FTE

Oct 2025 – Present · 8 mos · Pune, Maharashtra, India · On-site

Schneider electric

2 roles

Research And Development Intern

Feb 2025 – Aug 2025 · 6 mos · Bengaluru, Karnataka, India · Remote

As an R&D Intern, my primary achievement was architecting and developing an end-to-end AI pipeline to automate the architectural migration of legacy industrial control systems. This proof-of-concept tool intelligently refactors procedural logic (IEC 61131-3) into a modern, event-driven architecture (IEC 61499), preserving 90% of the original functionality and demonstrating a viable path for large-scale system modernization.
Architected a sophisticated six-stage pipeline of specialized AI agents (leveraging Deepseek, Transformer, and Qwen LLM models) to manage the entire conversion, from initial language analysis to final code synthesis and validation.
Engineered a novel "System Architect" module that utilizes an LLM to automatically refactor monolithic procedural code into a modular, component-based architecture, preserving 90% of functionality by intelligently generating the required internal data and event connections.
Developed a robust code generator with an integrated validation engine that proactively corrected over 600 categories of potential errors, ensuring all generated code was 90% compliant with the target standard.
Designed an intelligent mapping system that automated the conversion of common library functions and standard logic patterns, making the migration pipeline both scalable and highly efficient

Large Language Models (LLM)Software DesignSoftware DevelopmentArtificial Intelligence (AI)Deep Learning

Research And Development Intern

Feb 2025 – Aug 2025 · 6 mos · Bengaluru, Karnataka, India · Remote

PythonMachine LearningData AnalysisFeature Engineering

Mprass mining solutions

Machine Learning Intern

May 2024 – Jul 2024 · 2 mos · Udaipur, Rajasthan, India · On-site

Developed Predictive ML Models: Created machine learning models to analyze drone-collected geolocation data, achieving 85% accuracy in predicting soil suitability for mining.
Enhanced Mining Site Identification: Optimized data preprocessing and feature engineering, improving mining site identification efficiency by 20%.
Cross-Functional Collaboration: Worked closely with data engineers and domain experts to integrate ML insights, enhancing project outcomes by 30%.
Technologies & Tools: Python, Scikit-Learn, Pandas, NumPy, Drone Data Processing, Feature Engineering