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Emerging Technology Services Engineer

Job in Riyadh, Riyadh Region, Saudi Arabia
Listing for: Datamatics Technologies
Full Time, Seasonal/Temporary position
Listed on 2026-07-13
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), Cloud Computing: Infrastructure & Operations, Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 300000 - 520000 SAR Yearly SAR 300000.00 520000.00 YEAR
Job Description & How to Apply Below

Job Title: Emerging Technology Services Engineer
Experience: 6–11 Years
Location: Riyadh (Onsite)
Employment Type: Full-Time

Job Overview

We are seeking an experienced Emerging Technology Services Engineer with 6–11 years of experience to drive innovation initiatives, evaluate emerging technologies, and develop proof-of-concepts (PoCs) that support business transformation. The ideal candidate will have hands‑on experience in AI/ML experimentation, cloud-native technologies, automation, orchestration, and innovation enablement, with the ability to translate new technologies into scalable business solutions.

Key Responsibilities
  • Research, evaluate, and implement emerging technologies to address business and technical challenges.
  • Design, develop, and deliver AI/ML Proof of Concepts (PoCs) to validate new ideas and technologies.
  • Build cloud-native solutions that are scalable, resilient, and secure.
  • Design and implement automation and orchestration solutions to improve operational efficiency.
  • Collaborate with engineering, architecture, product, and business teams to identify innovation opportunities.
  • Assess technology trends and recommend adoption strategies aligned with organizational objectives.
  • Develop prototypes and pilot solutions to demonstrate business value.
  • Document solution architectures, technical findings, and best practices.
  • Mentor technical teams on emerging technologies and innovation frameworks.
  • Participate in architecture reviews and technology governance initiatives.
Required Technical Skills Artificial Intelligence & Machine Learning
  • Hands‑on experience with AI/ML PoC development
    .
  • Experience with machine learning model development, evaluation, and deployment.
  • Familiarity with Generative AI, Large Language Models (LLMs), and AI frameworks is preferred.
Cloud & Cloud-Native Technologies
  • Strong experience with cloud-native exploration and solution development
    .
  • Hands‑on experience with AWS or Microsoft Azure or Google Cloud Platform (GCP).
  • Experience with microservices, containers, and Kubernetes is preferred.
Automation & Orchestration
  • Strong experience in automation and orchestration using modern enterprise tools and scripting frameworks.
  • Experience building automated deployment, provisioning, and operational workflows.
  • Knowledge of Infrastructure as Code (IaC) is an advantage.
Innovation & Emerging Technologies
  • Experience driving innovation enablement through technology assessments, rapid prototyping, and pilot implementations.
  • Ability to evaluate emerging technology trends and recommend enterprise adoption strategies.
  • Experience working with innovation labs, R&D initiatives, or digital transformation programs is preferred.
Qualifications
  • Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related field.
  • 6–11 years of professional experience in software engineering, cloud technologies, AI/ML, or emerging technology initiatives.
  • Strong analytical, problem‑solving, and solution design capabilities.
  • Excellent communication and stakeholder management skills.
  • Experience working in Agile/Scrum environments.
Preferred Skills
  • Experience with Dev Ops and CI/CD pipelines.
  • Knowledge of API integrations and event-driven architectures.
  • Exposure to data engineering and analytics platforms.
  • Familiarity with cybersecurity best practices for cloud-native applications.
  • Cloud certifications and AI/ML certifications are a plus.
Key Technology Stack
  • Artificial Intelligence: AI/ML PoC development and Generative AI or Large Language Models (LLMs)
  • Cloud: AWS or Microsoft Azure or Google Cloud Platform (GCP)
    and cloud-native architectures
  • Automation: Automation and orchestration and Infrastructure as Code (Preferred)
  • Containers: Docker and Kubernetes
  • Dev Ops: CI/CD pipelines and Git-based version control
  • Innovation: Innovation enablement and rapid prototyping and technology evaluation and proof-of-concept development
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