Machine Learning Engineer

The objective of the Individual Contractor (IC) is to support the Arab Development Portal's work by designing, developing, and deploying state-of-the-art machine learning models applied to key development trends in the Arab region, with a focus on data-driven insights related to social, economic, and technological transformations. The IC will be responsible for building robust data pipelines, developing and maintaining ML models at scale, and implementing LLM-based and agentic-driven solutions to support DSDSD's innovation mandate.

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.

A bachelor's degree in computer science, data science, applied mathematics, statistics, or a related field is required. A master's degree in computer science, data science, applied mathematics, statistics, or a related field is desirable. All candidates must submit a copy of the required educational degree. Incomplete applications will not be reviewed. A minimum of 5 years of professional experience in machine learning engineering or a closely related discipline is required. Demonstrated proficiency in Python and core ML libraries (NumPy, pandas, scikit-learn, PyTorch or TensorFlow) is required. Experience designing and deploying end-to-end ML pipelines in production environments is required. Knowledge of LLMs and experience integrating them into data or analytical workflows is required. Experience with MLOps practices, experiment tracking, and pipeline orchestration tools (MLflow, Airflow, Prefect, or equivalent) is desirable. Familiarity with cloud or containerized ML deployment environments (Docker, Kubernetes, or cloud ML platforms) is desirable. Experience with NLP tasks and multilingual or Arabic language models is desirable.

English and French are the working languages of the United Nations Secretariat; and Arabic is a working language of ESCWA. For this position, fluency in English is required. Note: “Fluency” equals a rating of ‘fluent’ in all four areas (speak, read, write, and understand) and “Knowledge of” equals a rating of ‘confident’ in two of the four areas.

Not available.