AI/ML (Python) Developer

Akrisso; Gurgaon; On-site; INR 900,000 - 1,500,000; Full time

Job summary

Akrisso Innovations Private Limited is seeking a passionate AI/ML (Python) Developer for an on-site role in Gurgaon, Haryana. Exceptional freshers or early-career developers with strong Python fundamentals and curiosity about ML/AI are encouraged to apply.

You will work on developing AI features, evaluating models, processing data, and integrating AI into applications while exploring latest research. This is a full-time, onsite position with emphasis on fundamentals and hands-on implementation.

Qualifications

• Strong Python fundamentals and ML/AI curiosity.

Responsibilities

• Develop AI and ML functionality in Python.
• Work with modern ML frameworks and models.
• Build prototypes and experiments.
• Process and evaluate data.
• Integrate AI capabilities with applications.
• Evaluate performance and reliability.
• Research relevant advancements.
• Write clear, maintainable code.

Skills

Python

PyTorch or TensorFlow

Transformers

NLP

LLMs

APIs

Git

Data structures

Algorithms

Data processing

Tools

Docker

Linux

Job description

If the model gives you an answer, your next question should be: why?

Position: AI/ML (Python) Developer
Location: Gurgaon, Haryana, India
Experience:Exceptional Freshers / Early-Career Developers
Employment Type: Full-Time | Onsite

About The Company

Akrisso Innovations Private Limited is an AI technology company built around a simple belief: AI is only as powerful as the human mind directing it. We look for people who know their craft deeply, think logically, question the obvious and use AI as a tool to extend their ability, not replace it. If you rely on AI to think, create or code for you, Akrisso may not be the right place. We are looking for people with the judgement, curiosity and skill to shape AI, control it and use it to create work that would not be possible otherwise.

About This Role

We are looking for someone who is interested in AI below the surface.

Not simply:

Which API should I call?

But:

What is happening?

What assumptions are being made?

What does the data look like?

Why did performance change?

Why is the model failing here?

How do we measure whether something is actually better?

You should have strong Python fundamentals and genuine technical curiosity about machine learning and artificial intelligence.

Responsibilities:

What You'll Do

• Develop AI and machine-learning functionality in Python.
• Work with modern ML frameworks and models.
• Build prototypes and experiments.
• Process and evaluate data.
• Integrate AI capabilities with applications.
• Evaluate performance and reliability.
• Research relevant advancements.
• Write clear, maintainable technical code.

Preferred Qualifications:

Your Craft

You should have a practical foundation in several of:

• Python
• Data structures
• Algorithms
• PyTorch or TensorFlow
• Transformers
• NLP
• LLMs
• Data processing
• APIs
• Git

Knowledge of Linux, Docker, model deployment, statistics or mathematics is advantageous.

What We Notice

We notice people who:

• Read technical papers even when nobody requires them to.
• Have trained or experimented with models independently.
• Can explain overfitting rather than just define it.
• Think about evaluation instead of celebrating one impressive output.
• Enjoy understanding failure cases.
• Have changed their approach after evidence contradicted them.
• Care about fundamentals.

AI & Your Craft

Using AI to help you understand documentation, accelerate experimentation or examine ideas is sensible.

Using AI because you cannot program independently is not.

This is still an engineering role.

You should know Python.

You should understand the logic of your implementations.

You should understand the fundamentals of the models and techniques you work with.

We are not looking for someone whose AI experience begins and ends with LLM APIs.

You Might Not Enjoy This Role If

• You dislike programming.
• You avoid mathematics and technical fundamentals completely.
• You primarily copy notebooks.
• You rely on generated code you cannot explain.
• You are interested in AI only because it is currently popular.
• You stop investigating once an output looks impressive.