Machine Learning Platform Engineer

Delivery Hero; Dubai; On-site; AED 200,000 - 300,000; Full time

Job summary

A leading delivery services company in Dubai is seeking an ML Platform Engineer to develop robust machine learning platforms. The role involves collaborative work with data scientists and engineers to ensure seamless ML workflows, automate training pipelines, and maintain system reliability. Candidates should have a strong software engineering background, proficiency in Python, and experience with ML frameworks. This full-time role fosters a culture of engineering excellence, promoting efficiency and operational agility in ML initiatives.

Qualifications

• 3 years of experience in ML platform or related roles.
• Strong software engineering background with a focus on ML/AI.
• Proficient in Python, familiar with model operationalization.

Responsibilities

• Design, build, and maintain scalable ML platforms.
• Automate ML model training pipelines and deployment workflows.
• Ensure reliability and scalability of deployed ML workloads.

Skills

Software engineering

ML frameworks (e.g. TensorFlow, PyTorch)

CI/CD practices

SQL

Collaboration and communication

Education

Bachelor's degree in Computer Science or related field

Tools

Docker

Kubernetes

Cloud platforms (preferably GCP)

MLflow

Job description

Summary

As the leading delivery company in the region we have a great responsibility and opportunity to impact the lives of millions of customers restaurant partners and riders. To realize our potential we need to scale and evolve our machine learning capabilities across the company. This requires robust efficient and scalable ML platforms that empower teams to build deploy and operate intelligent systems with speed and reliability.

As an ML Platform Engineer your mission is to build and maintain the infrastructure and tooling that accelerates the development deployment and monitoring of machine learning models in production. Youll work closely with data scientists ML engineers and product teams to design seamless ML workflows from experimentation to serving and ensure a high standard of operational excellence in our ML systems.

Responsibilities

• Design build and maintain scalable reusable and reliable machine learning platforms and tooling to support the full ML lifecycle: data ingestion training evaluation deployment and monitoring.
• Collaborate with ML practitioners to understand their workflows and abstract them into flexible platform components and services.
• Automate and streamline ML model training pipelines model versioning artifact management and deployment workflows using modern MLOps practices.
• Integrate with infrastructure components (e.g. feature stores model registries experiment tracking orchestration engines) and cloudnative services to build robust systems.
• Ensure reliability observability and scalability of production ML workloads; implement monitoring alerting and performance evaluation for deployed models.
• Support reproducibility and governance in ML development by building infrastructure for metadata tracking lineage and auditability.
• Drive engineering best practices within the ML platform including CI/CD testing documentation and performance optimization.
• Partner with data engineering product and infra teams to align ML platform initiatives with broader company goals and architecture.
• Contribute to internal documentation onboarding and tooling adoption for data scientists and ML engineers.
• Champion a platform mindset and improve developer productivity by reducing friction in the ML workflow.

Requirements

Technical Experience

• Strong software engineering background with experience in building distributed systems or platforms ideally focused on ML/AI use cases.
• Proficiency in Python and experience with ML frameworks (e.g. TensorFlow PyTorch Scikitlearn) but with a focus on operationalizing rather than developing novel models.
• Handson experience with ML infrastructure tooling: model training and serving platforms (e.g. Vertex AI SageMaker Kubeflow MLflow Ray BentoML) orchestration frameworks (e.g. Airflow Flyte Dagster) and containerization (Docker Kubernetes).
• Familiarity with CI/CD pipelines version control and infrastructureascode (e.g. Terraform Helm).
• Experience working with cloud platforms preferably GCP (BigQuery Vertex AI GKE etc..
• Experience in building and managing feature stores model registries or experiment tracking systems is a plus.
• Strong understanding of model lifecycle management including monitoring for drift decay and data integrity.
• Solid SQL and familiarity with data warehouse modeling; experience with streaming or batch data pipelines is a plus.
• Understanding of the statistical and analytical needs of ML teams and the ability to translate them into scalable infrastructure.

Qualifications

• Bachelors degree in Computer Science Engineering or a related field. A postgraduate degree is a plus but not required.
• 3 years of experience in ML platform ML infrastructure or related roles.
• Proven track record of building systems that enable ML practitioners to ship models faster and with higher reliability.
• A system thinker with a product mindset and a passion for enabling others.
• An excellent collaborator with strong communication skills.
• High ownership pragmatism and a bias for action.

Remote Work

No

Employment Type

Fulltime