AI Team Lead

York Towers is the development arm of the York Holding Group. Through continuous research and the ability to predict emerging trends, the company keeps an edge over the market. Playing a leading role in driving the successful development and diversification of one of the most vital economic sectors, York Towers is committed to creating residential products across the country that provide residents with distinctive, universal, multicultural, and enriching lifestyles. York Towers is the exclusive developer of luxury real estate worldwide. The company aims to become a leading real estate player through an efficient business model and advanced technologies used for designing and construction. Since its establishment in 2016, York Towers has delivered 20 real estate properties and runs six dynamic projects. York Towers operates eight offices in five countries across three continents.

About Dwelleo

Dwelleo is an AI-powered real estate marketplace transforming how people search, buy, sell, and rent properties across Saudi Arabia.

The platform combines machine learning, intelligent discovery tools, and data-driven insights to connect buyers, renters, brokers, and developers through a seamless, scalable digital experience.

At its core, Dwelleo embeds AI directly into the product — powering pricing, recommendations, search, and decision-making across the entire property journey.

Job summary:

As AI Lead, you will own both our ML and LLM workstreams: defining the technical architecture, governing production systems, and leading the team that ships them. This is a hands-on leadership position — you are expected to be close to the technical decisions, not just the roadmap.

The immediate scope spans two areas: maturing our ML platform (pricing, forecasting, drift monitoring) and scaling our agentic AI systems into robust, production-grade infrastructure.

You will lead a team of 3–7 engineers, staying hands-on technically while owning delivery, standards, and team growth.

 

Main Functions & Responsibilities:

●       Own the full ML lifecycle — feature engineering, training, evaluation, deployment, and drift monitoring for pricing, rent, and ROI prediction models

●      Define the experimentation framework — data contracts, labelling strategies, A/B testing pipelines, guardrail metrics, and rollback procedures

●      Architect production agentic systems — design LLM-based multi-agent workflows with deterministic state machines, guardrail layers, and escalation logic

 ●      Lead infrastructure and platform decisions — FastAPI microservices on AWS ECS, model serving, CI/CD (GitHub Actions + MLflow), and end-to-end observability

●      Drive research and evaluation — assess new approaches across supervised learning, NLP, and agentic AI; decide what gets built, what gets dropped, and why

●      Lead a team of 3–7 engineers — set engineering standards, conduct code and design reviews, mentor team members, and participate in hiring as the technical voice

 

Qualifications & Requirements:

Required

●      6+ years of ML engineering experience, with at least 3 years in a technical lead or senior individual contributor role

●      Production-scale ML: supervised learning, gradient boosting (XGBoost / LightGBM), regression, feature engineering, and model evaluation

●      Solid MLOps practice: experiment tracking, model registry, canary deployments, drift detection, and incident response

●      2+ years working with LLM orchestration, RAG architectures, or multi-agent system design

●      Demonstrated experience leading a team of engineers — including hiring, mentoring, setting technical direction, and translating AI capabilities into product outcomes

 

Skills:

●      Leadership – guide and support the team to achieve goals

●      Clear communication – align technical and business teams

●      Decision-making – choose the right solutions and priorities

●      Teamwork – build a collaborative and positive team environment

●      Problem-solving – handle challenges and unblock the team

●      Strategic thinking – connect AI work with business goals