Machine Learning Platform and Engineering Lead

Overview

Lead ML platform engineering, MLOps, and analytics delivery at QuadReal.

Key Responsibilities

• ml lifecycle
• pipeline development
• mlops standards
• genai automation
• cloud optimization
• production ml

Tasks

-Establish and embed engineering standards across infra, data, and ML workflows. -Drive the ML lifecycle: data pipelines, feature engineering, model training, evaluation, deployment, and monitoring. -Build and maintain reusable pipelines, infrastructure templates, and app frameworks to accelerate delivery. -Closely collaborate with platform and engineering vendors to support the roadmap for advanced analytics capabilities. -Define and implement coding, testing, CI/CD, and MLOps standards across AADS. -Applying GenAI to engineering and data workflows to automate development and improve quality. -Balance rapid iteration for proofs-of-value (POVs) with robust, production-ready solutions. -Provide visibility into cloud usage and costs, driving efficiency. -Optimize cloud usage for cost and scalability. -Shape the roadmap for scaling advanced analytics and ML adoption across QuadReal. -Provide technical guidance and mentorship to platform engineers and data scientists. -Collaborate with product managers and stakeholders to align solutions with business priorities. -Design and deploy fit-for-purpose ML/AI solutions, selecting stacks based on not only benefits but also within tolerable risks, and limitations. -Deliver production-ready ML pipelines and applications that support investment and operations.

Requirements

• docker
• kubernetes
• mlflow
• azure
• 5+ years
• communication

What You Bring

-Experience with platform and vendor management for tools and integrations. -Proficiency with cloud infrastructure, containers (Docker/Kubernetes), and orchestration frameworks. -Familiarity with ML lifecycle tooling (versioning, monitoring, explainability). -5+ years in machine learning engineering, MLOps, or data/ML platform engineering, with strong software engineering foundations. -Exposure to agentic evaluations and responsible AI design. -Strong communication skills to bridge technical and business teams. -Experience designing and deploying GenAI architectures (e.g., RAG, agentic workflows, embedded LLM services). -Experience building data apps and workflows (Plotly Dash, FastAPI, LiteLLM, n8n or similar). -Prior mentoring or light management experience. -Proven track record deploying ML models to production using open-source stacks (MLflow, Airflow, Kubeflow, Prefect, Feast) and Azure hybrid environments.

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About Quadreal Property Group

-Fuses in-house investment, development, and property management under one roof for agile, integrated projects. -Portfolio spans office towers, logistics hubs, shopping centres, and residential communities worldwide. -Hallmark projects blend global strategy with local character, reshaping cities while driving strong institutional returns.

Sector Specialisms