Aryan Gupta

AI Researcher

Bengaluru, Karnataka, India1 yr 3 mos experience

Most Likely To SwitchAI ML Practitioner

Key Highlights

Experienced in optimizing AI/ML workflows.
Proficient in deploying models for edge devices.
Strong background in data analysis and visualization.

Stackforce AI infers this person is a SaaS-focused AI/ML developer with strong data analysis skills.

Contact

guptaaryan1010@gmail.com LinkedIn

Skills

Other Skills

AlgorithmsAnalytical SkillsBERT (Language Model)C (Programming Language)C++CommunicationCompetitive ProgrammingComputer ScienceComputer VisionConvolutional Neural Networks (CNN)Critical ThinkingDSADashboardsData AnalysisData Manipulation

Experience

1 yr 3 mos

Total Experience

7 mos

Average Tenure

1 yr

Current Experience

Zanskar research

2 roles

C++ Analyst

Jun 2025 – Present · 1 yr · Bengaluru, Karnataka, India · On-site

C++ Dev Intern

Jan 2025 – May 2025 · 4 mos · Bengaluru, Karnataka, India · On-site

Strategy

Zupee

MLE Intern

Sep 2024 – Dec 2024 · 3 mos · Gurugram, Haryana, India · On-site

Data Team:
Text-to-SQL and Workflow Enhancement: Optimized Text-to-SQL pipelines, integrated Bedrock with SageMaker, and advanced SQLSmith for query generation, conversational workflows, and analytics validation.
Model Evaluation and Optimization: Researched and evaluated super-resolution (SwinIR, Real-ESRGAN, GFPGAN) and VTON models for efficiency and edge-device inference. Enhanced latency, user profile generation, and documented findings

Oneplus

Innovation (AI/ML) Intern

Jul 2024 – Aug 2024 · 1 mo · Hyderabad, Telangana, India · On-site

AI & ML Team:
Deployments for edge devices for style transfer and RAG applications through FastAPI
Conducted extensive research by reading and analyzing multiple research papers on cutting-edge SoTA techniques in image enhancement. along with taking inferences

Google summer of code

Software Developer

May 2023 – Aug 2023 · 3 mos · Remote

@OSGeo
The project aims to implement a new function called "pgr_dijkstraDD" that replaces the existing "pgr_drivingDistance" function. The new function will have all overloads, including single and multiple vertices as before. The function will return columns such as sequence, depth, start vertex ID, node, edge, cost, and aggregate cost for all overloads. The project will also include testing of the new function with pgTap Tests. Documentation for the new function and migration guides for users to switch to the new function will also be created.