Overview
The Fullstack AI Engineer will support UN Global Pulse’s data innovation portfolio by contributing to the development, testing, and responsible application of data and AI solutions for sustainable development and humanitarian impact.
Tasks Summary
- Support the co-design, preparation, and implementation of data and AI innovation activities.
- Assist in developing data pipelines and systems, including data ingestion, cleaning, integration, and transformation.
- Assist in the development and maintenance of analytical and machine learning pipelines.
- Support training ML models and identify/test relevant open-source and partner-provided models.
- Implement mechanisms for model performance experiments tracking.
- Support deploying ML models and implement model monitoring systems.
- Assist with identifying and co-building solutions with external surge-capacity partners.
- Apply basic MLOps and open-source tools.
- Support research and analysis on emerging data science and AI trends.
- Assist with integrating analytical outputs and prototype results into UNGP initiatives.
- Assist in the design, testing, iteration, and refinement of responsible and high-impact data and AI solutions.
- Help ensure that data and AI solutions are developed with a focus on rapid prototyping, iterative improvement, and readiness for practical deployment.
- Assist in preparing knowledge products, brief analytical summaries, simple dashboards and visualisations.
- Contribute to knowledge integration and capacity-building efforts by supporting the documentation of methods, lessons learned, and good practices.
- Support the execution of data analysis, machine learning, and prototyping activities.
Experience Requirements
- Relevant experience is defined as experience in one or more of the following areas: data science, machine learning, data analytics, AI engineering or a related technical field.
- Experience in data preparation, exploratory analysis, feature engineering, or basic model development is required.
- Hands-on experience developing data-driven prototypes or analytical solutions, contributing to testing, documentation, and feature iteration is required.
Qualification Requirements
An advanced university degree (Master's or equivalent) preferably in computer science, data science, mathematics, statistics, or other related field OR A first-level university degree (Bachelor's or equivalent) preferably in computer science, data science, mathematics, statistics, or other related field with 2 years of relevant experience
