ML Engineer at Master-Works

Riyadh, Riyadh Region, Saudi Arabia -

Full Time

Start Date

Immediate

Expiry Date

30 May, 26

Salary

0.0

Posted On

01 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Model Deployment, MLOps, Python, R, Feature Engineering, Model Training, Production Environments, ML Pipelines, Model Monitoring, TensorFlow, PyTorch, Scikit-learn, Docker, Kubernetes, Cloud Platforms

Industry

IT Services and IT Consulting

Description

This is a highly skilled Machine Learning Engineer to design, build, deploy, and scale machine learning models that power data-driven products and intelligent systems. This role sits at the intersection of data science, software engineering, and MLOps, and requires strong hands-on experience turning models into production-ready solutions, programming experience in Python or R. Key Responsibilities: Design, develop, train, and optimize machine learning models for real applications or use cases. Translate business and product requirements into scalable ML/AI solutions. Implement feature engineering, model selection, tuning, and evaluation techniques. Develop , and deploy ML models into production environments with high availability and performance. Build and maintain ML pipelines (training, validation, deployment, monitoring). Monitor model performance, data drift, and model decay; retrain models as needed. Ensure models meet reliability, scalability, and security standards. Work closely with Data Scientists, Product Managers, and Software Engineers. Collaborate with data engineering teams to ensure high-quality, reliable data pipelines. Participate in design and code reviews, ensuring engineering best practices. Optimize models for latency, throughput, and cost. Implement experimentation frameworks (A/B testing, offline evaluation). Apply responsible AI principles, including fairness, explainability, and governance where required. 3–7+ years of hands-on experience in Machine Learning or applied AI roles. Strong programming skills in Python (and/or Java, Scala). Solid understanding of ML algorithms (supervised, unsupervised, deep learning). Experience with frameworks such as TensorFlow, PyTorch, Scikit-learn. Experience deploying models using Docker, Kubernetes, or cloud ML services. Strong knowledge of data structures, algorithms, and software engineering principles. Experience working in agile, cross-functional teams. Experience with cloud platforms (AWS, Azure, or GCP) and managed ML services. Hands-on experience with MLOps tools (MLflow, Kubeflow, Airflow, SageMaker, Azure ML). Experience with big data technologies (Spark, Kafka, Databricks). Background in NLP, Computer Vision, or Generative AI. Strong problem-solving and analytical thinking Production-first mindset Data-driven decision making High Collaboration and communication skills

Responsibilities

The role involves designing, building, training, and optimizing machine learning models for real-world applications and translating business needs into scalable ML/AI solutions. Key tasks include deploying models into production, building and maintaining ML pipelines, and monitoring model performance for reliability and security.