Aryan Rastogi

Data Engineer

Noida, Uttar Pradesh, India1 yr 10 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Delivered AI-driven solutions saving $53K annually.
Built a Contract Intelligence Tool in two weeks.
Optimized data engineering workflows reducing costs.

Stackforce AI infers this person is a Data Engineer specializing in AI and Machine Learning solutions.

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Skills

Core Skills

Machine LearningData PipelinesNatural Language Processing (nlp)Data Structures

Other Skills

Retrieval-Augmented Generation (RAG)Microsoft Copilot StudioAzure Key VaultPython (Programming Language)MLOpsCI/CD pipelinesReactJSAI model deploymentAlgorithmsMicrosoft AzureVector DatabasesLarge Language Models (LLM)Data MaintenanceData AnalysisEnglish

About

VIT 2024 Btech Spl in Data Science, Competitive coder on CodeChef

Experience

1 yr 10 mos

Total Experience

1 yr 10 mos

Average Tenure

1 yr 10 mos

Current Experience

Shell

Data Engineer

Aug 2024 – Present · 1 yr 10 mos · Bengaluru, Karnataka, India · On-site

Worked on delivering and scaling AI-driven and data-intensive solutions that improved decision-making, operational efficiency, and system reliability. Contributed to BallotGPT, an AI-powered document intelligence platform, enabling faster and more accurate information extraction using MLOps, CI/CD pipelines, Azure Key Vault, and secure cloud deployment, while driving $53K in annual cost savings through infrastructure optimization.
Built and showcased a Contract Intelligence Tool (POC) within two weeks, enabling AI-powered contract extraction, comparison, and summarization. This work strengthened hands-on experience with Copilot Studio, ReactJS, and prompt engineering, demonstrating rapid delivery of business-aligned GenAI solutions.
Improved security, scalability, and reliability of production systems by leading a major refactor of a Python codebase supporting 6,000+ annual requests, achieving zero downtime and no incident tickets since March 2025.
Modernized data engineering workflows by transitioning ETL pipelines from Alteryx to Python, significantly reducing licensing costs and delivering production-grade data pipelines within a Scrum-managed team.
Enhanced ML model reliability and forecasting accuracy through model monitoring and hyperparameter optimization, achieving a 6–7% error reduction and enabling automated daily model execution for traders in the EEX Tokyo baseload power futures market.

Retrieval-Augmented Generation (RAG)Microsoft Copilot StudioMachine LearningData PipelinesAzure Key VaultPython (Programming Language)

Boeing

Software Engineer

Jun 2023 – Aug 2023 · 2 mos · Bengaluru, Karnataka, India · On-site

AI-Powered Knowledge Retrieval System for Pilot & Crew Training
Built a centralized, cloud-based PDF knowledge repository for pilot and cabin crew training manuals, drastically reducing the time spent navigating large, complex documents.
Developed a ChatGPT-like natural language interface enabling users to query manuals in plain English and instantly receive context-aware answers, improving training efficiency and reducing cognitive load.
Implemented intelligent image retrieval to surface relevant diagrams and procedural visuals dynamically, enhancing understanding and learning outcomes.
Ensured strict data confidentiality and regulatory compliance by deploying the AI model locally from Hugging Face, with all processing performed entirely within the secure environment—no sensitive data leaving the platform.
Documented system architecture, technical workflows, and user guides to support long-term scalability, maintainability, and smooth knowledge transfer.

Retrieval-Augmented Generation (RAG)Natural Language Processing (NLP)

Samsung india

Research And Development Intern

Dec 2022 – May 2023 · 5 mos · Remote

Worked on optimizing the space and time complexity of BE Tree expression evaluations to improve overall system performance.
This involved closely analyzing existing evaluation logic to identify computational bottlenecks and redundant operations impacting efficiency. Under the guidance of experienced Samsung developers, I refactored data structures and optimized traversal and execution flows to enhance performance.
These improvements reduced memory usage and execution latency while maintaining correctness and scalability, contributing to faster processing, better resource utilization, and a more reliable system in production environments.

Data StructuresAlgorithms