AI Engineer

The client is looking for an AI Engineer (MLOps) to work in The United Arab Emirates.

About the Role:

As an AI Engineer (MLOps) in the Solution Evaluation & Incubation team, you will serve as the technical foundation for evaluating and integrating third-party AI solutions. In collaboration with Product Managers, Data Scientists and AI Architects, you will be responsible for designing and managing infrastructure, building data pipelines, integrating with external APIs and supporting the evaluation of vendor AI technologies. Your work will directly influence how external AI solutions are tested, validated and scaled within the organization’s enterprise ecosystem. You’ll play a critical role in ensuring a smooth transition from Proof of Concept (POC) to production-grade deployment, with a strong focus on MLOps, architecture, automation and technical due diligence.

Key Responsibilities:

• POC Environment & Infrastructure Setup:
• • Design and implement secure, scalable environments (cloud or on-prem) for evaluating AI vendor solutions.
  • Provision tools, libraries, and configurations needed for testing and benchmarking.
  • Ensure compliance with internal security and data governance standards.
• Data Engineering & Pipeline Management:
• • Build robust data pipelines to prepare and deliver relevant datasets (historical, synthetic) for evaluations.
  • Collaborate with Data Scientists to understand data requirements and ensure integrity and usability.
  • Enable secure, auditable data transfer to/from vendor platforms and APIs.
• Vendor Solution Integration:
• • • Integrate vendor AI solutions (APIs, SDKs, containers) into evaluation environments.
    • Troubleshoot deployment and compatibility issues during POCs.
    • Support experimental execution by ensuring seamless technical enablement.
• Automation & Operational Efficiency:
• • Develop automation scripts to streamline data handling, environment provisioning, and results processing.
  • Build reusable tools/components for future evaluations to improve speed and efficiency.
  • Maintain an internal knowledge base of best practices and common technical patterns.
• Architecture & MLOps Strategy:
• • Assess the technical architecture and MLOps maturity of vendor solutions during evaluations.
  • Identify risks and gaps for future integration into enterprise systems.
  • Provide foresight into deployment, monitoring, model drift, CI/CD, and scalability concerns.
• Collaboration & Documentation:
• • Collaborate with Product Managers, AI Architects, IT, and Data Science teams.
  • Document technical setups, pipeline workflows, and integration processes.
  • Contribute to evaluation reports, capturing lessons learned and technical recommendations.

Minimum Qualifications:

• Bachelor’s degree in computer science, Software Engineering, Data Engineering, or related field.
• 3+ years of experience in software engineering, data engineering, or MLOps roles.
• Proficient in programming languages such as Python, Java, or Scala.
• Hands-on experience with data pipeline tools like Apache Airflow, Spark, Kafka, or cloud-native equivalents.
• Proficient with cloud platforms (AWS, Azure, GCP) and associated data/AI services.
• Experience with API integration, containerization (Docker/Kubernetes), and DevOps workflows.
• Strong problem-solving, debugging, and communication skills.

Preferred Qualifications:

• Experience in a dedicated MLOps or AI Engineering role, with exposure to production ML model deployment and monitoring.
• Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Exposure to healthcare systems and data standards (e.g., EHRs, FHIR, PACS, HL7).
• Understanding of data security, privacy regulations, and governance in regulated industries.
• Experience with CI/CD tools, infrastructure-as-code (e.g., Terraform, CloudFormation).
• Knowledge of SQL/NoSQL databases, data lakes, and data warehousing technologies.