Machine Learning Engineer

Organizational Context

The Decision-Support and Data Science Division (DSDSD) is part of ESCWA's modernization and innovation efforts, providing advanced analytics and decision-support services to ESCWA, UN entities, and Member States. Aligned with the UN 2.0 agenda, DSDSD uses data-driven insights and emerging technologies to inform policymaking and operations, fostering strategic innovation across the region.

Job Purpose

The Machine Learning Engineer will support the Arab Development Portal by designing, developing, and deploying advanced machine learning models and LLM-based solutions to analyze key development trends in the Arab region. This role is crucial for building robust data pipelines, maintaining ML models at scale, and implementing innovative agentic-driven solutions that align with DSDSD's mandate. The engineer will contribute to data-driven decision-making, enhance organizational efficiency, and catalyze strategic innovation by leveraging emerging technologies and providing advanced analytics.

Responsibilities

Develop, train, evaluate, and deploy supervised and unsupervised ML models for forecasting, classification, clustering, anomaly detection, and NLP on regional datasets. Implement and maintain ETL pipelines for data ingestion and integration from diverse sources. Create reproducible ML experiments using tools like MLflow. Apply feature engineering and model optimization techniques. Benchmark and integrate LLMs for enhanced data analysis and content generation, including document understanding and question answering for Arabic and multilingual content. Explore and implement agentic solutions using deep research architectures. Design and implement RESTful APIs for ML model integration. Ensure model scalability, maintainability, and containerization. Collaborate with data scientists and engineers, and contribute to technical reports and presentations.

Work Experience

A minimum of 5 years of professional experience in machine learning engineering or a related discipline is required. Experience designing and deploying end-to-end ML pipelines in production environments is necessary. Knowledge of LLMs and their integration into analytical workflows is required. Experience with MLOps practices, experiment tracking, and pipeline orchestration tools is desirable.

Skills

Proficiency in Python and core ML libraries (NumPy, pandas, scikit-learn, PyTorch/TensorFlow). Experience with LLM integration. Experience with MLOps, experiment tracking, and pipeline orchestration tools (e.g., MLflow, Airflow). Familiarity with cloud/containerized ML deployment (Docker, Kubernetes). Experience with NLP tasks and multilingual/Arabic language models.

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