AI / ML Engineer Intern: (LLM & Agentic AI)

AI/ML Engineer Intern – LLM Training & Agentic AI

**Horrazon AI Horrazon Intelligence Private Limited**

• Internship Duration: 6 Months
• Employment Type: Full-time Internship
• Work Mode: Remote
• Department: Artificial Intelligence & Machine Learning
• Experience Level: Experienced Candidates Only
• Future Opportunity: Potential Full-Time Employment Based on Exceptional Performance
• Application Email: [email protected]

About Horrazon AI

Horrazon AI is building intelligent systems that transform complex business data into actionable decisions. Our focus is on developing advanced language models, agentic AI systems, and production-grade intelligence infrastructure that help businesses understand their data, identify opportunities, and act with greater precision.

At the core of our technology is Pogee, our LLM initiative, supported by an intelligent agent layer designed to process information, reason across multiple sources, and generate decision-oriented insights.

Our platform connects business intelligence with AI-driven execution, working across data from platforms such as Instagram, Facebook, LinkedIn, YouTube, and X.

Horrazon is supported by startup and technology ecosystems, including NVIDIA Inception, AWS Startup, and Zoho for Startups. We operate with a lean engineering culture where technical ownership, measurable outcomes, and production quality matter more than titles or hierarchy.

We are building for real-world deployment—not demonstrations that never leave a notebook.

The Opportunity

We are seeking an experienced and technically strong AI/ML Engineer to work on LLM training and fine-tuning, model evaluation, and agentic AI engineering.

This opportunity is intended for graduates who already possess substantial hands-on experience building, training, fine-tuning, or evaluating machine learning and LLM systems.

We are not looking for someone whose experience is limited to prompt engineering, API integration, tutorials, or introductory AI projects. We expect applicants to understand the underlying technology, explain their engineering decisions, and demonstrate the ability to build reliable systems.

You do not need a fixed number of years in the industry. However, you must have meaningful practical experience and be prepared to demonstrate your technical depth through an engineering assessment and interview.

During the six-month engagement, you will work directly with the founding and engineering team on real product development, with responsibilities spanning experimentation, implementation, evaluation, optimization, and production deployment.

Important Eligibility Requirements

Please read before applying.

• Graduates only: Applicants must have completed their undergraduate degree or an eligible postgraduate degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical discipline.
• Undergraduate students are not eligible: Candidates currently pursuing their bachelor’s degree, including students seeking internships alongside their studies, should not apply.
• Experienced candidates only: This is not an entry-level training programme. Applicants must have demonstrable practical experience in LLMs, machine learning, or closely related engineering work.
• Full-time commitment is mandatory: Candidates must be available to work full-time for the entire six-month internship.
• Technical evidence is essential: Applicants must provide GitHub repositories, research work, model-training experiments, deployed projects, technical reports, or other credible evidence of their capabilities.

Applications that do not meet these minimum requirements may not be considered.

Key Responsibilities
1. LLM Training and Fine-Tuning

• Fine-tune open-source language models using techniques such as LoRA, QLoRA, and parameter-efficient fine-tuning.
• Evaluate the suitability of full fine-tuning, supervised fine-tuning, and preference-optimization methods, including DPO and RLHF-style approaches, for specific product requirements.
• Develop reproducible training pipelines and experiment configurations.
• Diagnose training instability, overfitting, data-quality issues, and model-performance limitations.
• Make evidence-based decisions about model selection, adaptation, and deployment.

1. Transformer Architecture and Model Engineering

• Apply a strong understanding of transformer architectur…