Machine Learning Engineer

Client of United Nation careers

Background

This position is located in the Decision-Support and Data Science Division (DSDSD). The Division is part of ESCWA's broader modernization and innovation efforts, providing advanced analytics and decision-support services within ESCWA, other UN entities, and Member States. Aligned with the UN 2.0 agenda and grounded in strategic foresight, DSDSD leverages data-driven insights, emerging technologies, and scenario-based planning to anticipate trends and proactively inform policymaking and operations. Its core functions include data integration and management; data quality assurance; advanced analytics and modeling; machine learning and artificial intelligence solutions; real-time dashboards and performance reporting; and data visualization and business intelligence; and the design and development of specialized digital decision-support tools. Through these capabilities, DSDSD empowers evidence-based decision-making, fosters organizational efficiency, and catalyzes strategic innovation across the region.

Tasks and Responsibilities:

The Machine Learning Engineer will be responsible for the following tasks:

1. Machine Learning Model Development

• Design, train, evaluate, and deploy supervised and unsupervised ML models for tasks including forecasting, classification, clustering, anomaly detection, and natural language processing on regional datasets.
• Implement and maintain ETL pipelines for data ingestion, transformation, and integration from heterogeneous sources including national statistics, UN databases, and open data platforms.
• Develop reproducible and version-controlled ML experiments using tools such as MLflow, DVC, or equivalent platforms.
• Apply feature engineering, model selection, hyperparameter tuning, and ensemble techniques to optimize model performance across diverse problem domains.

2. LLM Integration and Agentic Solutions

• Explore and benchmark alternative large language models (open-source and proprietary) within the ADP's research environment to enhance data analysis, classification, and content generation processes.
• Implement LLM-powered pipelines for document understanding, information extraction, and question answering on Arabic and multilingual content.
• Explore and implement agentic solutions by leveraging deep research architectures and multi-step reasoning frameworks, adapting them to ESCWA's operational needs.

3. API Integration and Technical Infrastructure

• Design and implement RESTful APIs to expose ML model outputs and integrate them with the ADP's broader data ecosystem and digital tools.
• Ensure model scalability, maintainability, containerization readiness, and thorough documentation for long-term institutional use.
• Collaborate with data engineers to align ML outputs with downstream reporting, dashboard, and visualization requirements.

4. Collaboration and Reporting

• Collaborate with data scientists, engineers, and domain experts across DSDSD to ensure effective communication, data sharing, and alignment with project goals.
• Prepare and contribute to technical reports, presentations, and documentation explaining methodologies, model performance, and findings to both technical and non-technical audiences.