About DataFuel

DataFuel is an eCommerce automation and analytics platform powering real businesses at scale. Our systems process large volumes of production data and drive Decision making through reliable backend infrastructure and intelligent automation.

We are a product-first engineering team focused on building scalable systems, clean abstractions, and high-performance services. We value engineers who think deeply, take ownership, and build with intent.

About the Role

We are looking for a Backend-Focused Engineering Intern with strong fundamentals and curiosity for modern AI systems (agentic workflows, LLM-based reasoning systems, etc.).

This is not a “task-based” internship — you will work on real production systems, contribute to core backend logic, and help build intelligent systems that interact with data, APIs, and automation pipelines.

You should be someone who:

 Enjoys solving complex problems independently

 Can take a problem statement and drive it to execution

 Is curious about how modern AI systems are architected (not just used)

What You’ll Work On

Build and extend backend services using Python + FastAPI

Design and implement APIs powering production features

Work with PostgreSQL for structured data modeling and querying

Develop asynchronous workflows and background processing systems

Contribute to systems that involve LLMs, agent workflows, and reasoning pipelines

 Integrate and work with AWS services (S3, EC2, RDS, etc.)

 Debug production issues and optimize performance bottlenecks

 Collaborate across backend + frontend (TypeScript) for feature delivery

 Write clean, testable, and maintainable code

What We’re Looking For

Core Requirements

Strong fundamentals in Python

Good understanding of backend development (FastAPI / Flask / Django)

Solid grasp of APIs, HTTP, and web architecture

Working knowledge of SQL and relational databases

Ability to debug, reason, and think in systems

 Comfortable using Git and version control

AI / Systems Thinking (Important)

• Basic understanding of LLMs and how they are used in applications

• Familiarity with concepts like:

Prompting / structured outputs

Agent workflows (planning → execution → iteration)

Tool usage / function calling

• Interest in building AI systems, not just consuming APIs

Mindset (Very Important)

High ownership — you can take a problem and drive it end-to-end

 Ability to work with minimal hand-holding

 Strong problem-solving attitude

 Willingness to learn fast and adapt in a production environment

Nice to Have (Bonus)

Experience with async Python / concurrency

Exposure to AWS or any cloud platform

Understanding of Docker / deployment basics

Familiarity with TypeScript or frontend basics

 Interest in system design and scalability

What You’ll Gain

Work on a live production SaaS produc

Hands-on experience with scalable backend systems

Exposure to real-world AI system design (agentic workflows, reasoning systems)

Mentorship from experienced engineers

 Opportunity for full-time conversion based on performance

 A steep learning curve — technically and professionally

Who Should Apply

This role is ideal for someone who:

Has built a few projects and wants to go deeper into real systems

Is not satisfied with tutorials and wants real engineering challenges

 Wants to work at the intersection of backend systems + applied AI