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Selected role

Senior AI Engineer (Arabic Speaker)

Datamatics Global Services Ltd الرياض, S01, SA

Senior Full-time

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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