Machine Learning Engineer

Role Overview

We are looking for a skilled, hands-on Machine Learning Engineer with extensive experience in production environments to develop and deploy multiple ML solutions for the rental real estate market across the UK and Europe. Key projects will involve dynamic pricing algorithms, personalized recommendation engines similar to those used in e-commerce, predictive models for rental prices and occupancy rates, and sentiment analysis. This position is a crucial early hire in the company’s GCC expansion, offering rapid progression opportunities toward leadership roles.

Key Responsibilities

• Design, build, and implement production-level ML models including dynamic pricing, personalized recommendations, automated property valuation, rent and occupancy forecasting, and sentiment analysis.
• Create algorithms to detect pricing anomalies and react to real-time supply-demand trends to influence revenue management.
• Identify micro-location attributes that affect rental price growth and construct automated price-setting models.
• Develop computer vision systems for analyzing property images, processing floor plans, and assessing property conditions automatically.
• Construct robust MLOps pipelines incorporating model training, version control, continuous integration/deployment, monitoring, and drift detection.
• Set up model monitoring to proactively identify performance declines and data quality problems.
• Optimize ML model performance for low latency, high throughput, and cost-efficiency once deployed.
• Collaborate extensively with UK and European teams to effectively translate business challenges into machine learning solutions.

Technical Skills Required

• Proficiency in Python with strong software engineering principles and ability to write production-quality code.
• Deep familiarity with ML frameworks such as PyTorch or TensorFlow, and hands-on experience with scikit-learn, XGBoost, and LightGBM.
• Experience in AI techniques for fine-tuning models in a dynamic environment.
• Competency in MLOps tools including MLflow or Weights & Biases for model versioning, A/B testing, and drift monitoring within production systems.
• Knowledge of cloud platforms like AWS, Azure, or GCP for deploying ML models at scale.
• Use of containerization technologies such as Docker and Kubernetes to streamline model deployment and manage CI/CD workflows.
• Documented success building dynamic pricing models in domains like e-commerce, retail, travel, hospitality, or finance.

Ideal Candidate Profile

• Minimum of 3 years of experience deploying machine learning models in live production environments.
• Demonstrated capability serving models with over 10,000 predictions per day achieving sub-100 millisecond latency.
• Proven experience crafting dynamic pricing models or recommendation systems is essential.
• Knowledge across diverse ML areas including natural language processing, document AI, computer vision, and time series forecasting.
• Preferably background in finance, retail, or e-commerce sectors.
• Motivated to assume leadership responsibilities and mentor junior colleagues as the team scales.

Eligibility

Applicants should hold a B.Tech or B.E. degree in any engineering specialization.