Discovered MENA company
Healthcare AI Engineer
Location: Dubai (relocation support provided)
As an AI Engineer, 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 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 broader technology architecture and MLOps practices, ensuring a smooth transition from POC to pilot and potential scale.
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 (Machine Learning Operations) framework.
• 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 clinical or 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.
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.
Bonus:
• 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 and with healthcare data/systems (e.g., EHRs, LIS, PACS, FHIR).
• 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 a regulated environment.
• Ability to work effectively in a fast-paced, collaborative team environment.
