AI/ML Engineer

Delhi NCR (Hybrid)Full-time2-4 YearsAI

Extend the Dynoz AI platform on the LLM side, from data sourcing and processing to fine-tuning models. You will apply both engineering rigour and original thinking to make our AI more natural and consistent in production.

Responsibilities

Support sourcing, processing, and preparing high-quality training datasets for LLM fine-tuning.
Fine-tune open-source language models using modern techniques, adapting based on what the problem demands.
Evaluate model behavior, output quality, and conversational performance across real hospitality interaction scenarios.
Work across the AI pipeline, voice agents, chat agents, and the underlying LLM layer, to improve end-to-end intelligence.
Bring original thinking to fine-tuning strategy: experiment, form hypotheses, and iterate on what makes models perform best.

Requirements

2-4 years of hands-on experience in AI and ML engineering with a strong focus on LLMs.
Prior experience in fine-tuning LLMs using PEFT methods.
Solid understanding of training data pipelines, sourcing, cleaning, formatting, and quality evaluation for fine-tuning.
Familiarity with AI agent frameworks such as Pipecat.
Startup mindset, comfortable working at the frontier, solving problems, and moving quickly.

Benefits

High ownership and direct collaboration on the vision.
Competitive compensation.
Opportunity to lead the AI function as Dynoz scales.

Stack

PythonPipecatPyTorchHugging Face TransformersUnslothPEFT

Why Dynoz

What you would be joining

Dynoz is an AI-powered guest engagement system for hospitality. Our mission is to transform how the industry serves guests and travelers through AI. Our vision is a world where seamless service is the standard across every property, platform, and travel brand.

Responsibilities

Support sourcing, processing, and preparing high-quality training datasets for LLM fine-tuning.

Fine-tune open-source language models using modern techniques, adapting based on what the problem demands.

Evaluate model behavior, output quality, and conversational performance across real hospitality interaction scenarios.

Work across the AI pipeline, voice agents, chat agents, and the underlying LLM layer, to improve end-to-end intelligence.

Bring original thinking to fine-tuning strategy: experiment, form hypotheses, and iterate on what makes models perform best.

Requirements

2-4 years of hands-on experience in AI and ML engineering with a strong focus on LLMs.

Prior experience in fine-tuning LLMs using PEFT methods.

Solid understanding of training data pipelines, sourcing, cleaning, formatting, and quality evaluation for fine-tuning.

Familiarity with AI agent frameworks such as Pipecat.

Startup mindset, comfortable working at the frontier, solving problems, and moving quickly.