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AI​/ML Engineer

Job in Riyadh, Riyadh Region, Saudi Arabia
Listing for: Datamatics Technologies
Full Time, Seasonal/Temporary position
Listed on 2026-07-13
Job specializations:
  • Software Development
    Machine Learning/ ML Engineer, AI Engineer (Applied/Software), Cloud Engineer - Software
Salary/Wage Range or Industry Benchmark: 180000 - 320000 SAR Yearly SAR 180000.00 320000.00 YEAR
Job Description & How to Apply Below
Position: AI / ML Engineer

Job Title: AI / ML Engineer
Experience: 3–11 Years
Location: Riyadh (Onsite)
Employment Type: Full-Time

Job Description

We are seeking a skilled AI / ML Engineer with 3–11 years of experience to design, develop, deploy, and optimize machine learning and generative AI solutions. The ideal candidate will have hands‑on expertise in building scalable AI/ML models, working with cloud‑native AI platforms, and implementing production‑ready machine learning pipelines. Experience with modern AI frameworks, large language models (LLMs), and MLOps practices is highly desirable.

Key Responsibilities
  • Design, develop, train, and deploy machine learning and deep learning models for enterprise applications
  • Build and optimize end‑to‑end ML pipelines for data ingestion, model training, evaluation, and deployment
  • Develop Generative AI and LLM‑powered applications using modern AI frameworks
  • Collaborate with data engineers, software developers, and business stakeholders to deliver AI‑driven solutions
  • Deploy and monitor ML models on cloud platforms while ensuring scalability, reliability, and security
  • Optimize model performance through feature engineering, hyperparameter tuning, and continuous evaluation
  • Implement MLOps best practices including model versioning, monitoring, and CI/CD automation
  • Stay current with advancements in AI, machine learning, and cloud AI services
Required Technical Skills Cloud AI Platforms
  • Hands‑on experience with GCP Vertex AI or Azure Machine Learning or AWS Sage Maker
  • Experience with Azure OpenAI or AWS Bedrock for Generative AI solutions
  • Experience with Big Query ML and Dataflow for data processing and machine learning workflows
Programming & Machine Learning
  • Strong proficiency in Python
  • Experience developing machine learning solutions using Tensor Flow or Py Torch
  • Strong understanding of supervised, unsupervised, reinforcement learning, and deep learning concepts
Generative AI & LLM Frameworks
  • Experience with Hugging Face and Lang Chain for building LLM‑powered applications
  • Knowledge of prompt engineering, Retrieval‑Augmented Generation (RAG), embeddings, and vector databases is preferred
Data Engineering & Analytics
  • Experience with Databricks for data engineering, model development, and analytics workflows
  • Strong understanding of data preprocessing, feature engineering, and large‑scale data processing
MLOps & Deployment
  • Experience deploying machine learning models into production
  • Knowledge of Docker, Kubernetes, CI/CD pipelines, and model monitoring is an advantage
Qualifications
  • Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field
  • 3–11 years of professional experience in AI, Machine Learning, or Data Science
  • Strong analytical, mathematical, and problem‑solving skills
  • Experience working in Agile development environments
  • Excellent communication and collaboration skills
Preferred Skills
  • Experience with large language models (LLMs) and generative AI applications
  • Knowledge of Retrieval‑Augmented Generation (RAG), vector databases, and AI agents
  • Experience with distributed model training and cloud‑native AI architectures
  • Cloud certifications in AWS, Azure, or Google Cloud are a plus
Key Technology Stack
  • Cloud AI: GCP Vertex AI or Azure Machine Learning or AWS Sage Maker
  • Generative AI:
    Azure OpenAI or AWS Bedrock and Large Language Models (LLMs)
  • Data Processing:
    Big Query ML and Dataflow and Databricks
  • Programming:
    Python
  • Machine Learning Frameworks:
    Tensor Flow or Py Torch
  • LLM Frameworks:
    Hugging Face or Lang Chain
  • MLOps:
    Docker and Kubernetes and CI/CD (Preferred)
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