Staff Engineer - ML Platform

talabat; Dubai; On-site; AED 120,000 - 200,000; Full time

Job summary

A leading delivery platform in Dubai is seeking an experienced ML Platform Engineer to design and build scalable ML infrastructures. This role involves collaborating across teams to deliver efficient ML workflows for both traditional and generative AI models. The ideal candidate has strong expertise in Python, cloud infrastructure, and modern MLOps practices. Excellent communication skills and a strategic mindset are essential for driving platform adoption and improvement.

Qualifications

• 2+ years in a tech lead role.
• 5+ years of experience in ML platform engineering or related roles.
• Solid understanding of ML monitoring and model lifecycle management.

Responsibilities

• Design, build, and maintain scalable ML platforms.
• Implement CI/CD pipelines and automate model workflows.
• Collaborate with teams to align ML initiatives with company goals.

Skills

Python

Cloud infrastructure (AWS/GCP)

Docker

Kubernetes

ML frameworks (TensorFlow, PyTorch)

Education

Bachelor’s degree in Computer Science or related field

Tools

MLflow

Kubeflow

Ray

Job description

Job Description

Summary

As the leading delivery platform in the region, we have a unique responsibility and opportunity to positively impact millions of customers, restaurant partners, and riders. To achieve our mission, we must scale and continuously evolve our machine learning capabilities, including cutting-edge Generative AI (genAI) initiatives. This demands robust, efficient, and scalable ML platforms that empower our teams to rapidly develop, deploy, and operate intelligent systems.

As an ML Platform Engineer, your mission is to design, build, and enhance the infrastructure and tooling that accelerates the development, deployment, and monitoring of traditional ML and genAI models at scale. You’ll collaborate closely with data scientists, ML engineers, genAI specialists, and product teams to deliver seamless ML workflows—from experimentation to production serving—ensuring operational excellence across our ML and genAI systems.

Responsibilities

Design, build, and maintain scalable, reusable, and reliable ML platforms and tooling that support the entire ML lifecycle, including data ingestion, model training, evaluation, deployment, and monitoring for both traditional and generative AI models.

Develop standardized ML workflows and templates using MLflow and other platforms, enabling rapid experimentation and deployment cycles.

Implement robust CI / CD pipelines, Docker containerization, model registries, and experiment tracking to support reproducibility, scalability, and governance in ML and genAI.

Collaborate closely with genAI experts to integrate and optimize genAI technologies, including transformers, embeddings, vector databases (e.g., Pinecone, Redis, Weaviate), and real-time retrieval-augmented generation (RAG) systems.

Automate and streamline ML and genAI model training, inference, deployment, and versioning workflows, ensuring consistency, reliability, and adherence to industry best practices.

Ensure reliability, observability, and scalability of production ML and genAI workloads by implementing comprehensive monitoring, alerting, and continuous performance evaluation.

Integrate infrastructure components such as real-time model serving frameworks (e.g., TensorFlow Serving, NVIDIA Triton, Seldon), Kubernetes orchestration, and cloud solutions (AWS / GCP) for robust production environments.

Drive infrastructure optimization for generative AI use-cases, including efficient inference techniques (batching, caching, quantization), fine-tuning, prompt management, and model updates at scale.

Partner with data engineering, product, infrastructure, and genAI teams to align ML platform initiatives with broader company goals, infrastructure strategy, and innovation roadmap.

Contribute actively to internal documentation, onboarding, and training programs, promoting platform adoption and continuous improvement.

Requirements

Technical Experience

Strong software engineering background with experience in building distributed systems or platforms designed for machine learning and AI workloads.

Expert-level proficiency in Python and familiarity with ML frameworks (TensorFlow, PyTorch), infrastructure tooling (MLflow, Kubeflow, Ray), and popular APIs (Hugging Face, OpenAI, LangChain).

Experience implementing modern MLOps practices, including model lifecycle management, CI / CD, Docker, Kubernetes, model registries, and infrastructure-as-code tools (Terraform, Helm).

Demonstrated experience working with cloud infrastructure, ideally AWS or GCP, including Kubernetes clusters (GKE / EKS), serverless architectures, and managed ML services (e.g., Vertex AI, SageMaker).

Proven experience with generative AI technologies : transformers, embeddings, prompt engineering strategies, fine-tuning vs. prompt-tuning, vector databases, and retrieval-augmented generation (RAG) systems.

Experience designing and maintaining real-time inference pipelines, including integrations with feature stores, streaming data platforms (Kafka, Kinesis), and observability platforms.

Familiarity with SQL and data warehouse modeling; capable of managing complex data queries, joins, aggregations, and transformations.

Solid understanding of ML monitoring, including identifying model drift, decay, latency optimization, cost management, and scaling API-based genAI applications efficiently.

Qualifications

Bachelor’s degree in Computer Science, Engineering, or a related field; advanced degree is a plus.

2+ years in a tech lead role, 5+ years of experience in ML platform engineering, ML infrastructure, generative AI, or closely related roles.

Proven track record of successfully building and operating ML infrastructure at scale, ideally supporting generative AI use-cases and complex inference scenarios.

Strategic mindset with strong problem-solving skills and effective technical decision-making abilities.

Excellent communication and collaboration skills, comfortable working cross-functionally across diverse teams and stakeholders.

• Strong sense of ownership, accountability, pragmatism, and proactive bias for action.