Production ML Engineer: AI, LLMs & MLOps

Tanqeeb; Abu Dhabi; Hybrid; AED 320,000 - 540,000; Full time

Job summary

SAP in the United Arab Emirates is seeking a machine learning engineer to build and operationalize ML capabilities from idea to production. You will develop data pipelines, training and inference workflows, model-serving services, APIs, and evaluation frameworks for tasks such as recommendation, forecasting, and NLP.

You will strengthen the ML backbone with CI/CD, monitoring, and observability, work with PyTorch or TensorFlow, Spark, Kafka, Airflow, and feature stores, and contribute to

Qualifications

• 1-3+ years of experience in machine learning engineering or software engineering, with a track record of deploying models into production.
• Passionate about building reliable, scalable ML systems and delivering end-to-end solutions.
• Balance experimentation with engineering rigor to ensure models are production-ready and cost-aware.

Responsibilities

• Build and operationalize machine learning capabilities from idea to production.
• Develop data pipelines, training and inference workflows, model-serving services, APIs, and evaluation frameworks.
• Improve LLM-based workflows, including prompt design and retrieval-augmented generation.
• Strengthen the engineering backbone around ML through CI/CD, monitoring, observability, and testing.

Skills

Python

Java or Go

ML fundamentals

Deep learning

Data pipelines

Model deployment

Generative AI

MLOps

Spark

Kafka

Airflow

Feature Stores

Tools

PyTorch

TensorFlow

Spark

Kafka

Airflow

Feature Stores

Job description

SAP in the United Arab Emirates is seeking a machine learning engineer to build and operationalize ML capabilities from idea to production. You will develop data pipelines, training and inference workflows, model-serving services, APIs, and evaluation frameworks for tasks such as recommendation, forecasting, and NLP.

You will strengthen the ML backbone with CI/CD, monitoring, and observability, work with PyTorch or TensorFlow, Spark, Kafka, Airflow, and feature stores, and contribute to