DevOps & Machine Learning Expert

Specific Responsibilities Under the overall guidance of the DRM Program Head, the DevOps & Machine Learning Expert will undertake the following tasks: The hydrological forecasting chains in use at ICPAC running operationally in an automated forecast cycle, with new product releases deployed without interruption to service. Harmonised regional hydrological reference and observation datasets, with documented quality control and exchange interfaces for national hydrological services. Operational STAC API and analysis infrastructure for hazard, exposure and impact data, with reliable processing pipelines across all integrated models. East Africa Flood Watch in public operation at functional parity with Drought Watch, with its warnings and products integrated into the East Africa Hazard Watch Portal. Automated data processing and reporting workflows, including impact-based forecast bulletin generation with language-model-assisted drafting under documented evaluation and guardrails. Technical support provided to Member States, NMHSs and regional partners on IGAD flood monitoring activities, with a record of requests addressed. Calibrated post-processing and multi-model ensemble combination in operations, supported by a methodology note. Bayesian Network risk model and inference service deployed and integrated with the risk monitoring application. Event-based climate storylines contributed to the drought and flood event catalogue. Forecast verification framework with routine skill reporting, and documented governance for every operational model. Reproducible multi-cloud infrastructure as code with ecFlow and Prefect scheduling, CI/CD and GitOps delivery, observability in production, and a tested disaster-recovery procedure. Complete technical documentation, deployment guides, JupyterHub tutorials, training materials and stakeholder hand-over. Performs such other duties as may be assigned from time to time.

Key Educational Qualifications and Professional Experience

Academic Qualification

University degree in Computer Science, Geo-Informatics, Hydroinformatics, Computer Engineering, Data Science, Software Engineering, Information Technology or other relevant field; an advanced degree is an advantage.

Candidates should demonstrate their qualifications and proficiency in web application development and geo-application development (provide links to at least 2 samples of previous work and/or Github code)

Professional work experience

Minimum of four (4) years of relevant experience in geo-applications design and development.

Proficiency in web application development and geo-application development, demonstrated through a portfolio of developed products (at least two samples of previous work and / or GitHub code).

Demonstrated experience operating production cloud infrastructure under daily operational deadlines, and deploying machine learning models operationally rather than in research settings.

Experience supporting national institutions in an operational early warning context is desirable.

Scripting and automation of geoprocessing and large data workflows, especially in Python; sound knowledge of SQL and PostGIS.

Web technologies (HTML, CSS, JavaScript) and production-ready geospatial web applications using Node.js, React, Mapbox GL, Leaflet, GeoServer, MapServer and GDAL; REST API development and microservices architecture; experience with Go is an advantage.

OGC geospatial standards including WMS, WFS, WCS, WPS and Simple Features for SQL; handling and analysis of Earth Observation data in a range of formats.

STAC API and PySTAC; workflow management with ecFlow and Prefect; xarray, dask and the numpy ecosystem; rasterio and geopandas; GRIB2, Zarr, COG, VirtualiZarr, Icechunk and kerchunk; PostgreSQL/PostGIS and TimescaleDB.

Hydroinformatics: operationalisation of rainfall-runoff, hydrodynamic and rapid inundation forecasting chains; forcing preparation; catchment, river network and terrain data management; hydrometric and remotely sensed observation handling; and hydrological data standards and exchange.

Container orchestration (Docker, Kubernetes, Helm) and GitOps delivery (ArgoCD); multi-cloud computing on Google Cloud Platform and Amazon Web Services with Terraform, Coiled and CI/CD pipelines; CUDA and GPU environment management; observability, incident response, cloud security and cost management.

Impact-based forecasting systems and climate modelling workflows; ensemble post-processing, calibration and downscaling; Bayesian networks; forecast verification; MLOps practice; LLM integration, evaluation and guardrails; training and capacity development.

Essential Skills and Competencies Required

Self-driven, result-oriented, problem solver

Teamwork

Communication

Continuous improvement and knowledge sharing