Machine Learning Engineer

Location

United Arab Emirates

Company name

Property Finder

Sector

Technology

Contract duration

Full time

Salary (monthly)

Non-Dislcosed

Experience

2+ Years

apply now

About the role

We are looking for a skilled professional with expertise in Machine Learning Engineering (MLE Level II) and Data Science (Level II) to join our AI & Data Science team at Property Finder. This role will focus on developing and deploying advanced AI solutions using Generative AI, Large Language Models (LLMs), and transformer-based models to drive personalisation, automation and innovation across our platforms. The ideal candidate will have a strong foundation in machine learning, practical experience with transformer architectures, and the ability to collaborate across teams to deliver impactful AI-driven solutions. Join a forward-thinking team at the forefront of innovation, working on cutting-edge projects that leverage GenAI and Large Language Models (LLMs) to transform the real estate experience. Be part of a collaborative and dynamic environment that values continuous learning, technical excellence, and teamwork. With exposure to advanced AI technologies and challenging projects, you'll have the opportunity to grow professionally and make a meaningful impact in an ever-evolving industry.

Responsibilities

Deep understanding and working knowledge of deep learning and neural networks architectures
Design, fine-tune, and deploy Large Language Models (LLMs) and generative models tailored to business needs.
Develop applications using transformer-based architectures such as GPT, BERT, T5, or similar frameworks.
Implement use cases in personalization, content creation, and workflow automation using Generative AI.
Optimize LLM performance for inference in real-time or large-scale production environments.
Conduct research and experimentation to identify improvements in GenAI and LLM applications.
Build and maintain scalable ML pipelines to deploy LLMs and generative models efficiently.
Develop workflows for fine-tuning and serving transformer models in production.
Automate the deployment process using MLOps tools (e.g., Kubernetes, MLflow, Docker).
Optimize data pipelines and feature engineering processes to support transformer-based models.
Build and implement ML models for predictive analysis and personalization.
Collaborate with cross-functional teams to generate actionable insights and support business strategies.
Conduct data wrangling, feature engineering, and advanced statistical analysis.
Design and evaluate experiments (e.g., A/B testing) to validate model performance.
Partner with the GenAI team to align model development with business goals.
Work closely with MLE and Data Science teams to ensure seamless integration of LLMs and generative solutions into production workflows.
Collaborate with the Futurism team to explore cutting-edge AI applications and opportunities for LLMs.

Requirements

Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field.
2+ years of experience in machine learning engineering and data science roles, with 2+ years hands-on experience in transformers, LLMs and generative models.
Proficiency in fine-tuning and deploying transformer models (e.g., GPT, BERT, T5).
Familiarity with tools like Hugging Face Transformers, OpenAI APIs, and LangChain.
Expertise in prompt engineering and domain-specific fine-tuning of LLMs.
Knowledge of attention mechanisms and sequence-to-sequence modeling.
Strong experience with ML pipelines and MLOps tools (e.g., Kubernetes, Docker, MLflow).
Advanced SQL and Python programming skills.
Familiarity with cloud platforms (AWS, GCP, Azure) for scalable deployments.
Intermediate experience with supervised and unsupervised learning algorithms.
Proficiency in data wrangling and feature engineering.
Knowledge of statistical analysis and hypothesis testing.
Strong analytical and problem-solving abilities.
Effective communication skills to collaborate with cross-functional teams.
Adaptability to work in a fast-paced, dynamic environment.