AI​/ML Engineer

Model Evaluation

• Model Evaluation

Please read the JD carefully berore applying.

AI / ML Engineer (T1–T5)

Riyadh, Kingdom of Saudi Arabia (KSA)

Yes

0–12+ Years

We are seeking AI / ML Engineers across multiple experience levels (T1–T5) to design, develop, train, deploy, and optimize machine learning models and AI solutions throughout the complete machine learning lifecycle. Candidates will work on data preparation, feature engineering, model development, evaluation, deployment, monitoring, and continuous improvement using modern cloud AI platforms and open-source machine learning frameworks.

The role offers opportunities ranging from entry-level implementation to enterprise AI architecture and technical leadership.

• Design, develop, train, evaluate, and deploy machine learning and AI solutions
• Build scalable ML pipelines from data preparation through production deployment
• Develop supervised, unsupervised, deep learning, and generative AI models
• Perform feature engineering, data preprocessing, model validation, and hyperparameter optimization
• Integrate ML models into enterprise applications and cloud-native environments
• Deploy AI models using managed cloud ML services and MLOps practices
• Monitor model performance, drift, accuracy, and production reliability
• Collaborate with Data Scientists, Data Engineers, Software Engineers, and Dev Ops teams
• Optimize model performance, scalability, and inference latency
• Document models, experiments, evaluation metrics, deployment processes, and governance standards
• Follow AI security, responsible AI, and model governance best practices

• GCP Vertex AI or Big Query ML or Dataflow
• Azure ML or Azure OpenAI
• AWS Sage Maker or Amazon Bedrock

• Python

• Tensor Flow or Py Torch

• Hugging Face or Lang Chain

• Databricks

• Machine Learning
• Deep Learning
• NLP
• Computer Vision
• Model Evaluation
• Feature Engineering
• API Development
• Git

Role Focus: Learning, implementation, and execution under supervision.

• Assist in data preparation, cleansing, and feature engineering
• Develop simple machine learning models using established frameworks
• Support model training, testing, and validation activities
• Deploy models under senior guidance
• Maintain documentation for datasets, experiments, and models
• Debug ML pipelines and resolve basic issues
• Learn cloud AI platforms and development best practices
• Follow coding standards, security policies, and project guidelines

Role Focus: Independent development and delivery.

• Build and deploy production-ready machine learning models
• Perform feature engineering and model optimization
• Develop reusable ML components and inference APIs
• Implement model evaluation and performance monitoring
  
  Integrate ML models into enterprise applications
• Collaborate with cross-functional engineering teams
• Troubleshoot production AI issues
• Contribute to model documentation and deployment automation

Role Focus: Technical ownership and solution development.

• Design end-to-end AI and machine learning solutions
• Lead development of complex ML pipelines and AI applications
• Optimize training pipelines for performance and scalability
• Guide junior engineers and perform technical reviews
• Implement Responsible AI, explainability, and governance practices
• Improve model reliability, monitoring, and lifecycle management
• Collaborate with business stakeholders to translate requirements into AI solutions
• Support architecture decisions for enterprise AI initiatives

Role Focus: Technical leadership and enterprise solution delivery.

• Lead architecture and delivery of enterprise AI platforms and machine learning solutions
• Define technical standards, reusable frameworks, and engineering best practices
• Lead multiple AI initiatives across business domains
• Drive cloud-native AI solution design and deployment
• Review solution architecture, model performance, and production…