Senior AI Engineer (Arabic Speaker)

Location: Riyadh, Saudi Arabia

Experience: 6–8 Years

Employment Type: Full-Time / Contract

Language Requirement: Native or Fluent Arabic Speaker (Mandatory)

About the Role

We are seeking a highly skilled Senior AI Engineer (Arabic Speaker) to join our growing AI and Data Science team in Riyadh. The ideal candidate will have strong expertise in Artificial Intelligence, Generative AI, Machine Learning Operations (MLOps), and Cloud-based AI Platforms, with proven experience in designing, developing, deploying, and managing enterprise-grade AI solutions.

The successful candidate will play a key role in building scalable AI systems, implementing GenAI applications, operationalizing machine learning models, and collaborating with business stakeholders to deliver innovative AI-driven solutions that create measurable business impact.

Key Responsibilities

AI & Machine Learning Development

• Design, develop, train, and deploy machine learning and deep learning models for enterprise use cases.
• Build and optimize predictive analytics, NLP, recommendation systems, and intelligent automation solutions.
• Develop AI-powered applications leveraging Large Language Models (LLMs) and Generative AI technologies.
• Fine-tune foundation models and implement Retrieval-Augmented Generation (RAG) architectures.
• Evaluate and benchmark AI models to ensure performance, scalability, and reliability.

Generative AI Engineering

• Design and implement enterprise GenAI solutions using OpenAI, Azure OpenAI, Claude, Gemini, Llama, Mistral, and other LLM platforms.
• Develop conversational AI solutions, intelligent assistants, and knowledge management systems.
• Build prompt engineering frameworks and optimize prompts for business use cases.
• Implement vector databases and semantic search solutions.
• Develop AI agents and autonomous workflows using modern AI orchestration frameworks.

MLOps & AI Operations

• Design and implement end-to-end MLOps pipelines for model training, deployment, monitoring, and lifecycle management.
• Automate model deployment using CI/CD pipelines and infrastructure-as-code practices.
• Monitor model performance, drift detection, retraining strategies, and operational KPIs.
• Establish AI governance, model versioning, reproducibility, and compliance standards.
• Implement scalable AI platforms supporting multiple business units.

Cloud & Platform Engineering

• Deploy AI/ML workloads on cloud platforms such as Azure, AWS, GCP, or OCI.
• Manage containerized AI environments using Docker and Kubernetes.
• Design scalable AI infrastructure supporting high-volume enterprise workloads.
• Optimize cloud resources, performance, and operational costs.

Data Engineering & Integration

• Collaborate with data engineering teams to build AI-ready data pipelines.
• Integrate AI solutions with enterprise applications, APIs, databases, and business platforms.
• Ensure data quality, security, privacy, and compliance with organizational standards.

Stakeholder Management

• Engage with business stakeholders to identify AI opportunities and translate business requirements into technical solutions.
• Present AI solution architectures, recommendations, and project outcomes to technical and non-technical audiences.
• Mentor junior AI engineers, data scientists, and platform engineers.

Required Technical Skills

Artificial Intelligence & Machine Learning

• Machine Learning
• Deep Learning
• Natural Language Processing (NLP)
• Predictive Analytics
• Computer Vision (preferred)
• Reinforcement Learning (preferred)

Generative AI

• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Prompt Engineering
• AI Agents & Multi-Agent Systems
• Fine-Tuning and Model Optimization
• Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS)
• LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen

MLOps

• MLflow
• Kubeflow
• Airflow
• Model Monitoring & Observability
• CI/CD for ML
• Feature Stores
• Model Registry
• Experiment Tracking
• Model Governance

Cloud Platforms

• Microsoft Azure
• AWS
• Google Cloud Platform (GCP)
• Oracle Cloud Infrastructure (OCI) – Preferred

Containers & DevOps

• Docker
• Kubernetes
• Git
• GitHub Actions
• Jenkins
• Terraform
• Infrastructure as Code

Programming Languages

• Python (Mandatory)
• SQL
• Bash/Shell Scripting
• Java or C# (Preferred)

Qualifications

• Bachelor's Degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related discipline.
• Master's Degree in AI, Machine Learning, Data Science, or related field is highly preferred.

Experience Requirements

• 6–8 years of experience in AI/ML Engineering, Data Science, or AI Platform Engineering.
• Minimum 3+ years of hands-on experience implementing Generative AI solutions.
• Proven experience building and operationalizing machine learning models in production environments.
• Strong experience implementing enterprise MLOps frameworks and practices.
• Experience working with cloud-native AI services and modern AI platforms.

Preferred Certifications

• Microsoft Azure AI Engineer Associate
• AWS Machine Learning Specialty
• Google Professional Machine Learning Engineer
• OCI AI Foundations Associate
• Kubernetes Certifications (CKA/CKAD)
• Databricks Machine Learning Professional

Soft Skills

• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder management abilities.
• Ability to work effectively in cross-functional and multicultural environments.
• Strong ownership, accountability, and leadership mindset.
• Passion for innovation and continuous learning.
• Ability to communicate fluently in both Arabic and English.

Mandatory Requirements

• Native or Fluent Arabic Speaker.
• 6–8 years of relevant AI/ML engineering experience.
• Hands-on expertise in Generative AI and MLOps.
• Strong Python programming skills.
• Experience deploying AI solutions in enterprise production environments.
• Willingness to work onsite in Riyadh, Saudi Arabia.

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