ARYAN PAHARIBACKEND & APPLIED AIBack to experience
ENGINEERING / EXPERIENCEAryan Pahari

Building what works.

Two internships. Production systems, applied AI, and practical engineering decisions.

Overview · Implementation · Evidence
01CURRENT / DEC 2025 — PRESENT

Omnisavant.ai — Software Development Engineer Intern

Working on production systems behind interactive product demos — improving how realistic the demos feel, how quickly mock environments can be created, and how product flows are turned into reproducible browser automation.

01

Made live demos more realistic by improving browser behaviour and flow fidelity, creating a closer-to-product experience for prospects. The work is live in production.

02

Built a programmatic sandbox-cloning and Claude-assisted mock-generation workflow that turns cloned product environments into usable mocks in roughly 3–6 hours. The workflow is live in production.

03

Contributed to the production demo orchestration pipeline, including demo-plan generation, Playwright script generation, workflow execution and validation.

PythonPlaywrightLangChainBackend automation
02PREVIOUS / APR — OCT 2025

amasQIS.ai — Artificial Intelligence Engineer Intern

Built backend and applied-AI features across two kinds of products: industrial analytics and an AI-powered virtual try-on experience. My work spanned data access, agent tooling, REST APIs, reporting, and model integration.

01

SQL-connected industrial assistant — Implemented data-retrieval tools using FastMCP and LangChain, integrating LLaMA 4 Scout to help analyze electrolyzer measurements and answer questions grounded in industrial data.

02

Automated technical reporting — Built diagnostic reporting flows that turned SQL results into visualizations and structured Quarto/PDF outputs, making technical analysis easier to review and share.

03

FastAPI backend for virtual try-on — Worked on authentication, user profiles, body measurements, settings, community posts and AI prompt/search endpoints; integrated Firebase services alongside Gemini, MediaPipe and OpenCV for pose-aware experiences.

04

Predictive-model and delivery pipelines — Worked with LSTM and XGBoost modeling, PyTorch-based experiments, FastAPI serving, Docker packaging and Airflow-based workflow orchestration.

FastAPIFastMCPLangChainSQLFirebaseGeminiMediaPipeOpenCVPyTorchXGBoostDockerAirflow