ML, Engineer

Master-Works; Riyadh; On-site; SAR 240,000 - 420,000; Full time

Job summary

Master Works is seeking a highly skilled Machine Learning Engineer to design, build, deploy, and scale ML models powering data-driven products and intelligent systems. The role intersects Data Science, Software Engineering, and MLOps, with hands-on experience turning models into production-ready solutions.

You will collaborate with DS, PM, SWE, and Data Eng teams to develop scalable AI solutions, optimize model performance, and support enterprise AI initiatives aligned with engineering best

Qualifications

• Bachelor's degree in a relevant field.
• 3–7+ years hands-on ML/AI experience.
• Strong Python and/or Java/Scala programming.
• Solid understanding of ML algorithms (supervised, unsupervised, DL).
• Hands-on experience with TF, PyTorch, Scikit-learn.
• Experience deploying ML models with Docker/Kubernetes or cloud ML services.
• Strong software engineering fundamentals (data structures & algorithms).
• Experience in Agile, cross-functional delivery teams.
• Experience with cloud platforms (AWS/Azure/GCP).
• Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, SageMaker, Azure ML).
• Familiarity with big data: Spark, Kafka, Databricks.
• Background in NLP, CV, or Generative AI is preferred.
• Strong analytical, problem-solving, collaboration, and communication skills.
• Experience building enterprise-scale AI/ML platforms.
• Familiarity with Responsible AI and AI governance practices.
• Experience supporting production-grade AI systems and high-scale ML deployments.

Responsibilities

• Design, develop, train, optimize, and deploy ML models for real-world business use cases.
• Translate requirements into scalable ML and AI solutions.
• Implement feature engineering, model selection, tuning, validation, and evaluation.
• Deploy ML models into production with high availability and performance.
• Build and maintain ML pipelines for training, validation, deployment, and monitoring.
• Monitor performance, data drift, and model decay; support retraining.
• Ensure ML solutions meet reliability, scalability, governance, and security standards.
• Collaborate with cross-functional teams including DS, PM, SWE, and Data Eng.
• Support development and maintenance of high-quality data pipelines.
• Participate in architecture reviews, design reviews, and code reviews.
• Optimize models for latency, throughput, scalability, and cost efficiency.
• Implement experimentation and evaluation frameworks (A/B tests, offline evals).
• Apply Responsible AI principles including fairness, explainability, governance.

Skills

Python

Java/Scala

Machine Learning

Supervised/Unsupervised/DL

Agile / cross-functional

Data Structures & Algorithms

Cloud platforms (AWS/Azure/GCP)

MLOps (MLflow/Kubeflow/Airflow)

Docker/Kubernetes

NLP/CV/Generative AI (pref)

Big data tools (Spark, Kafka, Databr"b

Education

Bachelor’s degree in Computer Science, AI, Data Science, Software Engineering, or related field

Tools

TensorFlow

PyTorch

Scikit-learn

Docker

Kubernetes

SageMaker/Azure ML/GCP ML services

Job description

Master Works is seeking for a highly skilled Machine Learning Engineer to design, build, deploy, and scale machine learning models that power data-driven products and intelligent systems. The role sits at the intersection of Data Science, Software Engineering, and MLOps, requiring strong hands‑on experience in transforming models into production‑ready solutions.

The Machine Learning Engineer will work closely with Data Scientists, Product Managers, Software Engineers, and Data Engineering teams to develop scalable AI solutions, optimize model performance, and support enterprise AI initiatives aligned with engineering best practices.

Key Responsibilities

• Design, develop, train, optimize, and deploy machine learning models for real-world business use cases.
• Translate business and product requirements into scalable ML and AI solutions.
• Implement feature engineering, model selection, tuning, validation, and evaluation techniques.
• Develop and deploy ML models into production environments with high availability, scalability, and performance.
• Build and maintain machine learning pipelines including training, validation, deployment, and monitoring workflows.
• Monitor model performance, data drift, and model decay, and support retraining and optimization activities.
• Ensure ML solutions meet reliability, scalability, governance, and security standards.
• Collaborate with Data Scientists, Product Managers, Software Engineers, and Data Engineers across cross‑functional teams.
• Support the development and maintenance of high‑quality and reliable data pipelines.
• Participate in architecture discussions, design reviews, and code reviews following engineering best practices.
• Optimize models for latency, throughput, scalability, and operational cost efficiency.
• Implement experimentation and evaluation frameworks including A/B testing and offline evaluations.
• Apply Responsible AI principles including fairness, explainability, governance, and model transparency where applicable.

• Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field.
• Minimum 3–7+ years of hands‑on experience in Machine Learning, Applied AI, or related technical roles.
• Strong programming experience in Python and/or Java, or Scala.
• Solid understanding of machine learning algorithms, including supervised learning, unsupervised learning, and deep learning techniques.
• Hands‑on experience with machine learning frameworks such as TensorFlow, PyTorch, and Scikit‑learn.
• Experience deploying ML models using Docker, Kubernetes, or cloud‑based ML services.
• Strong knowledge of software engineering principles, data structures, and algorithms.
• Experience working within Agile and cross‑functional delivery teams.
• Hands‑on experience with cloud platforms, including:
• Amazon Web Services AWS
• Microsoft Azure
• Google Cloud GCP
• Experience with MLOps tools and platforms such as MLflow, Kubeflow, Airflow, SageMaker, or Azure ML.
• Familiarity with big data technologies, including Spark, Kafka, and Databricks.
• Background in NLP, Computer Vision, or Generative AI is preferred.
• Strong analytical thinking, problem‑solving, collaboration, and communication skills.
• Experience building enterprise‑scale AI or ML platforms.
• Familiarity with Responsible AI and AI governance practices.
• Experience supporting production‑grade AI systems and high‑scale ML deployments.

Job Location: Client Site