Amarthya.s .george

Software engineer with hands-on experience building REST APIs, data pipelines, and backend automation in Python

Talent

BengaluruMember since September 8, 2026

PythonPrompt EngineeringGoogle ADKPandasNumPyMatplotlibSeabornscikit-learnFlaskREST APIsMicrosoft Copilot StudioPyTorchScapyServer-Sent Events

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PythonPrompt EngineeringGoogle ADKPandasNumPy

About

Software engineer with hands-on experience building REST APIs, data pipelines, and backend automation in Python. Comfortable working across the pipeline lifecycle, from data extraction and preprocessing to API integration and deployment, with a track record of debugging and optimizing pipeline components in production settings. Strong foundation in data structures, algorithms, and object-oriented design, paired with practical experience shipping reliable, tested systems rather than one-off scripts.

Eager to deepen expertise in concurrency, connector-level integration nuances, and large-scale data pipeline performance.

Experience

AI Automation Intern at Mresult (2026-01 – 2026-07)

Architected a six-agent, role-orchestrated document validation system for GDP compliance workflows and a semantic PDF diffing engine performing meaning-level, layout-agnostic comparison of document revisions, using Azure OpenAI GPT-4o via multi-agent Flask pipelines to automate cross-checking and gap flagging.
Designed and deployed a fully custom Microsoft Copilot Studio chatbot with topic-based conversation routing, retrieval-grounded knowledge base integration, and clean multi-flow handoff to resolve domain-specific stakeholder queries.
Implemented serverless AI workflow orchestration via Azure Functions with Key Vault and Managed Identity for secure credential management, and engineered structured prompt strategies to automate governance and reporting workflows.

AI Intern at Edutainer, PAT Technologies Pvt. Ltd. (2025-02 – 2025-05)

Developed and deployed a Flask web application with multi-model generative AI integration using Claude and Gemini APIs, implementing fallback routing logic and context-aware prompt pipelines entirely in Python.
Built an interactive ML visualization tool using Python and scikit-learn, enabling non-technical users to explore classification, regression, and clustering results through real-time visual output via Matplotlib and Seaborn.
Engineered a multi-model LLM pipeline for automated prompt refinement, using structured API calls and iterative post-processing to improve output quality across diffusion-based models.