Software Engineer Intern

Company Description

Aadhunik AI is a platform dedicated to making artificial intelligence accessible and practical for everyone, from beginners to business professionals. We provide a curated directory of AI tools organized by functionality, real reviews, and expert insights to help users make informed decisions. Our mission is to bridge the gap between AI and everyday users, empowering individuals and businesses with tailored AI-driven recommendations. Aadhunik AI is not just a platform but a community aimed at exploring the infinite possibilities of artificial intelligence and driving innovation. Join Aadhunik AI to stay ahead in the world of AI with simplified tools, resources, and expert guidance.

At Aadhunik, after years helping people navigate the noise of the AI revolution, from news to

tools to training. We realized that software alone isn't enough to solve the friction of modern life.

Today, we are bringing AI into the physical world. We are building a fleet of companions designed not

just to process data, but to elevate how we live. We have started work on our first robot, NIK, which is

more than a tool; it is a tireless, judgment-free partner built to help you master your day, protect your

focus, and live a better, more intentional life.

The Role

We are looking for a highly curious Software Engineering Intern to act as the primary architect for the

robot's Brain. You will be responsible for linking our hardware sensors (MIPI cameras, I2S

microphones) to localized AI models, ensuring the robot can see, hear, and converse with near-zero

latency.

Key Responsibilities

Voice & AI Integration: Write and maintain Python scripts that connect various AI models,

enabling the robot to listen, process language, and generate spoken responses entirely

offline.

Computer Vision Tasks: Work with live camera feeds and implement computer vision

algorithms (like object detection and facial recognition) so the robot can see and understand

its environment.

Software Optimization & Packaging: Help structure, package, and optimize our code so it runs

smoothly and efficiently on our physical edge computing hardware without lagging.

Hardware Communication: Write scripts that take software decisions (like locating a person's

face on camera) and translate them into basic data commands for our microcontrollers so the

robot can physically react and move.

Memory & Data Systems: Assist in building and managing local databases that allow the AI to

recall past interactions, giving the robot a sense of long-term memory.

General Engineering Support: Assist the core team with basic software debugging,

researching new open-source AI tools, and writing clean documentation for the code you

build.

What You Must Have (Requirements)

Currently pursuing a B.Tech/BE in Computer Science, AI/ML, Electronics, or a related field. Tier 1 Colleges Preferred.

Strong proficiency in Python (you should be very comfortable writing modular, clean code).

Experience navigating Linux operating systems via the command line (Ubuntu, SSH, basic

bash scripting).

A fundamental understanding of how to interact with APIs and open-source AI models

(HuggingFace, Ollama, etc.).

A scrappy, problem-solving mindsethardware development involves debugging complex,

undocumented errors.

Bonus Points (Nice to Have)

Previous experience working with Single Board Computers (Raspberry Pi, NVIDIA Jetson).

Experience with C++ or basic microcontroller programming.

A portfolio or GitHub profile showing personal projects related to AI, computer vision, or

hardware.

What We Offer

Experience in working on a lead role and being a part of a very tightknit team of 3.

Hands-on experience with hardware that is being assembled in-house (based on NVIDIA

Jetson Orin Nano).

The chance to see your code directly control a physical, autonomous product.

A Letter of Recommendation and a high-impact project to anchor your resume.

This is a Paid Internship with a stipend of 15-20K/month.

How to Apply

Please share your resume along with a link to your GitHub profile or a portfolio of relevant past

projects to [Confidential Information]

In your email, briefly mention one challenge you anticipate when running an LLM locally on edge hardware and how you might solve it.