Senior AI Engineer; Arabic Speaker

Apply for Senior AI Engineer (Arabic Speaker) at Datamatics Global Services Ltd in الرياض, S01, SA. This full‑time on‑site position offers great opportunities for career growth.

Senior AI Engineer (Arabic Speaker) –

Location:

Riyadh, Saudi Arabia
•

Experience:

6–8years
•

Employment Type:

Full‑Time / Contract
• Language Requirement:
Native or fluent Arabic speaker (mandatory).

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, ope rationalising machine learning models, and collaborating with business stakeholders to deliver innovative AI‑driven solutions that create measurable business impact.

• AI & Machine Learning Development – Design, develop, train, and deploy machine learning and deep learning models for enterprise use cases; build and optimise 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 optimise 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 infra‑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 containerised AI environments using Docker and Kubernetes; design scalable AI infrastructure supporting high‑volume enterprise workloads; optimise 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 organisational 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.

• 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.
• AI Orchestration & Language:
  Lang Chain, Llama Index, Semantic Kernel, CrewAI, Auto Gen.
• MLOps & Dev Ops: MLflow, Kubeflow, Airflow, Model Monitoring & Observability, CI/CD for ML, Feature Stores, Model Registry, Experiment Tracking, Model…