Fullstack AI Engineer
UNOPSFinland2 days ago
Full-timeEngineering, Information Technology
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Job Highlight
The incumbent will support UN Global Pulse’s data innovation portfolio, the Accelerator Programme, UN80 priorities and the DISHA programme. Under the supervision and technical guidance of the Technical Program Officer - Data and AI products for Humanitarian and Development Use will contribute to the development, testing, and responsible application of data and AI solutions for sustainable development and humanitarian impact. The role will include supporting co-building efforts with on-the-ground teams, engineers, academia, and private sector partners, and collaborating with contributors within and outside the UN system to address data, analytical, and research needs.
The incumbent will be a staff member of UNOPS under its full responsibility.
Role Purpose
Using diverse data sources, including open, private-sector, and UN datasets, the incumbent of the position will support data collection, cleaning, integration, exploratory analysis, and model preparation activities across the UN Global Pulse portfolio. The Fullstack AI Engineer will help the Data Science team identify trends and patterns, generate initial analytical outputs, contribute to prototype development, and support analytical work that informs policy and operational decision-making across UN entities and partners.
In doing so, the Fullstack AI Engineer will contribute to transforming early-stage concepts into evidence-based innovations, strengthening the organisation’s mission to accelerate the responsible use of data and AI for people and planet.
Functions / Key Results Expected
The Fullstack AI Engineer supports the execution of data analysis, machine learning, and prototyping activities that contribute to insights for UN Global Pulse initiatives.
Key results may include:
Required
Experience Requirements
Required:
The incumbent will support UN Global Pulse’s data innovation portfolio, the Accelerator Programme, UN80 priorities and the DISHA programme. Under the supervision and technical guidance of the Technical Program Officer - Data and AI products for Humanitarian and Development Use will contribute to the development, testing, and responsible application of data and AI solutions for sustainable development and humanitarian impact. The role will include supporting co-building efforts with on-the-ground teams, engineers, academia, and private sector partners, and collaborating with contributors within and outside the UN system to address data, analytical, and research needs.
The incumbent will be a staff member of UNOPS under its full responsibility.
Role Purpose
Using diverse data sources, including open, private-sector, and UN datasets, the incumbent of the position will support data collection, cleaning, integration, exploratory analysis, and model preparation activities across the UN Global Pulse portfolio. The Fullstack AI Engineer will help the Data Science team identify trends and patterns, generate initial analytical outputs, contribute to prototype development, and support analytical work that informs policy and operational decision-making across UN entities and partners.
In doing so, the Fullstack AI Engineer will contribute to transforming early-stage concepts into evidence-based innovations, strengthening the organisation’s mission to accelerate the responsible use of data and AI for people and planet.
Functions / Key Results Expected
The Fullstack AI Engineer supports the execution of data analysis, machine learning, and prototyping activities that contribute to insights for UN Global Pulse initiatives.
Key results may include:
- Data systems, workflows, or pipelines supported and maintained, ensuring reliable preparation, cleaning, and organisation of datasets for downstream analytical and ML tasks.
- Analytical summaries and exploratory data analysis supported to inform modelling decisions and support project teams with interpretable, actionable information.
- Contributions to the development and iteration of machine learning prototypes and MVPs for example by assisting with preparing datasets, running experiments, and maintaining clear notebooks or documentation for reuse and further development.
- Basic dashboards, visualisations, or monitoring materials developed, supporting continuous learning, performance tracking, and communication of key insights across projects.
- Contributions made to knowledge products, case studies, and documentation that support system learning and can be incorporated into reusable guidance or toolkits.
- Support provided in applying responsible AI principles, including assisting with transparency checks, data/ethical reviews, and documentation that supports risk-aware development.
Required
- 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
Experience Requirements
Required:
- 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.
- Experience using Python and SQL for data manipulation, analysis, or modelling.
- Experience working with version control tools such as Git and GitHub in collaborative workflows.
- Experience with DevOps workflows (e.g., CI/CD, GitHub Actions, etc.)
- Experience with LLMs (OpenAI API, LangChain, structured outputs, tool calling, evaluation)
- Experience with AI coding agents (Cursor, Claude Code, Codex, Github Copilot, Replit)
- Experience preparing data visualizations or analytical outputs using open-source or code-based tools (e.g., Plotly, seaborn, matplotlib, ggplot2, Quarto, Observable, or similar).
- Experience working with modern ML/AI tools or environments (e.g., PyTorch, TensorFlow, HuggingFace) is an asset.
- Familiarity with text analytics/NLP, geospatial data, or basic model evaluation techniques is desirable.
- Experience with UX development, user interviews, user testing, design thinking
- Experience with web development frameworks (React, Next.js, shad.cn)
- Experience with Jupyter or similar data science development tools is an asset.
- Familiarity with cloud platforms or data engineering environments (e.g., GCP, AWS, Azure) is desirable.
- Experience working with humanitarian, development, or public-sector datasets is an asset.
Key Skills
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