Amir Thapa Magar

AI Integration, Full-Stack & Infrastructure Engineer

I build AI-integrated products, research software, and infrastructure tools across the full stack: from product UX and APIs to model workflows, deployment, and hardware-adjacent prototypes.

7+years building software
30+projects shipped and prototyped
40+technologies across the stack
4domains: AI, full-stack, infrastructure, hardware
Engineering systemsAI product workflow

Document ingestion, grounded responses, human review, and deployment-ready product surfaces.

Next.jsPythonLLM
Product appsDeutschTrack + SurfBreak

Learning, focus, mobile UX, browser workflows, and polished user-facing systems.

InfrastructureHPC-Pilot

Research computing support, retrieval, documentation, and operational clarity.

Engineer, researcher, and product builder.

I am Amir Thapa Magar, a software engineer with a computer science research background and a product-builder mindset. My work spans AI workflows, full-stack systems, HPC and research infrastructure, mobile foundations, and applied machine learning.

The current focus is clear: build useful AI-integrated software with credible architecture, thoughtful UX, privacy-aware workflows, and deployment paths that can survive real users.

Selected work with real systems behind it.

The strongest projects sit first: AI workflows, HPC tooling, document intelligence, language learning, focus software, and research systems from my personal engineering practice.

caminode

Caminode, my software studio.

Caminode is the software studio side of my engineering practice: focused on SaaS, AI workflows, productivity software, technical tools, and products that respect real deployment constraints.

3D isometric Caminode studio workspace for AI integration, product engineering, and infrastructure workAI IntegrationFull-Stack EngineeringHPC & InfrastructureProduct Thinking

Approach: from workflow pressure to reliable software.

The work is not positioned as a generic skill list. Each capability is framed around product evidence, architecture, privacy, deployment, and human review.

01

Discover

Map the workflow, users, constraints, data sources, and risks before writing code.

02

Design

Turn the system into a usable product surface with clear states, review points, and guardrails.

03

Build

Ship typed interfaces, APIs, AI workflows, data handling, and deployment-ready code.

04

Operate

Validate with real inputs, source evidence, monitoring needs, and paths for iteration.

AI Integration & LLM Workflows

Practical AI features with agents, document processing, source-grounded responses, review flows, and privacy controls.

CrewAILangChainUnslothRAGOpenAIGemini

Full-Stack Product Engineering

From product interface to backend workflow, data model, deployment, and iteration-ready architecture.

Next.jsReactDjangoDRFTypeScriptPython

HPC & Research Infrastructure

Research-computing workflows, documentation systems, Linux-oriented operations, and cluster support tooling.

SLURMLinuxDockerGitLab CIPostgreSQL

Applied ML & Computer Vision

Model training, evaluation, inference APIs, sensor data, image classification, and on-device ML prototypes.

PyTorchTensorFlow LiteScikit-learnPandasPlotly

Mobile & Cross-Platform Apps

Native and cross-platform application delivery for polished mobile experiences, prototypes, and production workflows.

SwiftSwiftUIKotlinReact NativeAndroid

Hardware-Adjacent Systems

Sensor workflows, embedded foundations, data logging, microcontroller prototypes, and hardware/software integration.

ArduinoC/C++SensorsIoTData Logging

Earlier product engineering archive.

The mobile and embedded work stays visible as career foundation, but it no longer dominates the primary story.

Research and infrastructure work.

HPC support, applied machine learning, document intelligence, embedded foundations, and computer-vision experiments that support the current AI and infrastructure positioning.

Certifications and focused learning.

A compact view of the training themes that support the portfolio: applied AI, full-stack systems, infrastructure, mobile, and embedded engineering.

Machine Learning & Deep Learning

Applied AI coursework and project practice

Model training, evaluation, computer vision, sensor data, and practical inference workflows.

Full-Stack Product Engineering

Professional project portfolio

Typed frontends, backend services, APIs, deployment, privacy-aware document workflows, and SaaS product UX.

Cloud, DevOps & Infrastructure

Research and production systems

Docker, Linux operations, CI workflows, HPC documentation, and deployment-ready engineering habits.

Mobile & Embedded Foundations

Earlier engineering track

iOS/Android delivery, hardware-adjacent data logging, sensors, and on-device ML experimentation.

Services I can help with.

This is the practical engagement layer of EngineeringByAmir: software, AI, infrastructure, and hardware-adjacent builds with clear technical ownership.

AI Integration & Automation

Document intelligence, LLM workflows, review systems, source-grounded answers, privacy scrubbing, and useful internal tools.

LLMsRAGPDF workflowsHuman review

Web Applications

SaaS MVPs, dashboards, portals, admin tools, backend APIs, and polished interfaces that can grow from prototype to product.

ReactNext.jsDjangoDRFPostgreSQL

Mobile Applications

Native and cross-platform mobile apps, product prototypes, app-store-oriented features, and mobile-first UX flows.

SwiftSwiftUIKotlinReact NativeFirebase

HPC & Research Software

Research-computing portals, cluster support tooling, scientific workflows, documentation systems, and Linux-oriented operations.

HPCSLURMDockerLinux

Hardware & Embedded Systems

Sensor data logging, microcontroller-oriented prototypes, hardware/software integration, and data pipelines for applied ML.

SensorsArduinoC/C++Data logging

Let's craft your vision into reality.

Currently available for hiring and freelance work.