AI Engineer

Marc Ellis; United Arab Emirates; On-site; AED 220,000 - 294,000; Full time

Job summary

A technology consulting company in the United Arab Emirates is looking for an AI Engineer to support the evaluation of AI solutions. The ideal candidate should have a background in software engineering or data engineering, with strong programming skills, experience in data pipeline management, and proficiency with cloud platforms. This role offers the opportunity to work on innovative projects in a dynamic team environment.

Qualifications

• Minimum 3 years experience in software engineering or data engineering.
• Proficiency in programming languages such as Python, Java, or Scala.
• Experience with cloud platforms (AWS, Azure, or GCP) and their AI services.

Responsibilities

• Design and implement scalable environments for AI solution evaluations.
• Develop and maintain robust data pipelines for POC evaluations.
• Facilitate the technical integration of vendor AI solutions into evaluation environments.

Skills

Programming in Python

Data engineering

MLOps principles

API integration

Problem-solving

Education

Bachelor's degree in Computer Science

Tools

Apache Airflow

Docker

AWS

Kubernetes

Job description

Overview

Role: AI Engineer

Location: Dubai, ONSITE

Duration: 6 months extendable contract

Notice Period: Immediate joined to Max 30 days official notice period

AI Engineer – Solution Evaluation & Incubation

As an AI Engineer in the Solution Evaluation & Incubation team, you will be the technical backbone supporting the rapid evaluation of a wide array of third-party AI solutions. You will work in close collaboration with Product Managers and Data Scientists to design, build, and maintain the infrastructure, data pipelines, and integration points necessary to conduct rigorous Proof of Concept (POC) evaluations.

Your primary focus will be on enabling efficient and scalable testing of vendor solutions against organizational data and requirements. This includes setting up evaluation environments, managing data flows, integrating with vendor APIs or platforms, and ensuring the technical feasibility of POCs.

Critically, you will also contribute a forward-looking perspective, considering how successfully evaluated solutions might integrate into the broader technology architecture and MLOps practices, ensuring a smooth transition from POC to pilot and potential scale.

Key Responsibilities

1. POC Environment & Infrastructure Setup:
   • Design and implement scalable and secure environments (cloud-based or on-premise as appropriate) for conducting AI solution evaluations.
   • Configure and manage necessary tools, libraries, and frameworks required for vendor solution testing and data analysis.
   • Ensure appropriate data access controls, security measures, and compliance with data governance policies within evaluation environments.
2. Data Engineering & Pipeline Management for Evaluation:
   • Develop and maintain robust data pipelines to ingest, transform, and prepare data (historical, synthetic) for use in POC evaluations.
   • Work with Data Scientists and Product Managers to understand data requirements for specific evaluations and ensure data quality and integrity.
   • Implement solutions for efficient data transfer to/from vendor platforms or APIs, adhering to security and compliance standards.
3. Vendor Solution Integration & Technical Enablement:
   • Facilitate the technical integration of vendor AI solutions (APIs, SDKs, containerized models) into evaluation environments.
   • Troubleshoot technical issues related to vendor solution deployment, data compatibility, and API connectivity during POCs.
   • Support Data Scientists in running experiments by ensuring the technical setup allows for efficient execution and result collection.
4. Automation & Efficiency:
   • Develop scripts and tools to automate repetitive tasks in the evaluation process (e.g., data preparation, environment provisioning, results aggregation).
   • Contribute to building a library of reusable components and best practices for rapid POC setup and execution.
   • Continuously seek opportunities to improve the speed, efficiency, and scalability of the AI solution evaluation process.
5. Architectural Foresight & MLOps Considerations:
   • During POCs, assess the technical architecture of vendor solutions with an eye towards future integration into enterprise systems and MLOps frameworks.
   • Identify potential challenges and requirements for scaling successful solutions (e.g., performance, monitoring, model retraining, data drift).
   • Provide technical input on how evaluated solutions can be embedded into existing business workflows and the broader technology stack.
6. Collaboration & Documentation:
   • Work closely with Product Managers, Data Scientists, AI Architects, and IT operations teams.
   • Document technical setups, integration procedures, data pipelines, and lessons learned from each POC.
   • Contribute to the technical aspects of evaluation reports and recommendations.

Qualifications & Experience

Minimum Qualifications:

• Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, or a related technical field.
• 3+ years of experience in a software engineering, data engineering, or MLOps role.
• Proficiency in programming languages such as Python, Java, or Scala.
• Experience with building and managing data pipelines using tools like Apache Airflow, Spark, Kafka, or cloud-native ETL/ELT services.
• Hands-on experience with cloud platforms (AWS, Azure, or GCP) and their data and AI services.
• Familiarity with containerization technologies (e.g., Docker, Kubernetes).
• Experience with API development and integration.
• Strong problem-solving and troubleshooting skills.

Preferred Qualifications:

• Experience specifically in an MLOps or AI engineering role, with a focus on deploying, monitoring, and managing machine learning models.
• Familiarity with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Experience working in the healthcare industry or with regulated data systems.
• Knowledge of data warehousing, data lake, and database technologies (SQL and NoSQL).
• Experience with CI/CD pipelines and infrastructure-as-code (e.g., Terraform, CloudFormation).
• Understanding of data security and privacy principles, especially in regulated environments.
• Ability to work effectively in a fast-paced, collaborative team environment.