AI/ML Engineer

Locations: Addis Ababa, Ethiopia

Deadline: 27 July 2026 (Early Application are encouraged as the position may close once a suitable candidate is found).

Job Description

Project: Data.FI Ethiopia

Palladium is a global company working to design, develop and deliver positive impact on the lives and livelihoods of people around the globe; broaden access to health, water, power, and infrastructure; build enduring, sustainable, and transformative institutions and market systems to address global challenges; and conserve the natural world. We operate in over 50 countries and have a workforce of more than 2,000 talented and motivated staff around the world.

Palladium is part of GISI’s global family of companies, which aims to create solutions for the world’s most complex challenges. With annual revenues of $14 billion, GISI’s approximately 15,000 employees are engaged in projects across 100 countries worldwide providing construction, program/project management, and engineering consulting services.

Project Overview:

Data.FI is a global project funded by the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR) through the U.S. Department of State (DoS). The goal of Data.FI is to accelerate and sustain access to high-quality data to expedite HIV epidemic control, as well as epidemics across other health areas.

The project is DoS‘ primary mechanism for strengthening data, technology, and HIS to support HIV activities. Data.FI aims to improve global, regional, and national in-depth analysis of HIV epidemiologic and programmatic data. Such analyses can then be directly applied to expedite the achievement of PEPFAR targets. Data.FI’s focus also includes direct support to host-country governments to enhance existing HIS, thereby informing management responses to well-defined gaps in HIV and AIDS programming and sustaining impact by supporting the transition of activities to local partners.

By the end of Data.FI, an advanced analytic approach for program implementation and scale-up will be institutionalized in PEPFAR countries and led by local organizations. HIV data systems will be supported by domestic investments and local capacity and be flexible to meet emerging decision-making needs critical to accelerating and sustaining epidemic control.

The AI/ML Engineer will support the technical team in enriching a data warehouse platform with practical machine learning and AI capabilities. The role will focus on identifying realistic AI/ML use cases, preparing data for modelling, developing prototype models or AI-enabled workflows, and demonstrating how a modern data warehouse can support predictive analytics, intelligent automation, and decision support.

This role is suited for someone with strong experience in machine learning engineering, MLOps, cloud-based ML systems, and a practical understanding of data warehouses and data pipelines.

·   Design and develop AI/ML components that integrate into a modern data warehouse environment.

·   Collaborate with the data warehouse and ETL team to understand available data sources, warehouse structures, data quality constraints, and candidate AI/ML use cases.

·   Identify practical AI/ML opportunities including anomaly detection, forecasting, risk scoring, data quality validation, entity matching, classification, and natural language processing where applicable.

·   Develop efficient, explainable, and problem-solving models or AI-enabled workflows that are accessible to both technical and non-technical stakeholders.

·   Support MLOps practices including model versioning, reproducibility, monitoring, and documentation.

·   Work across data engineering, architecture, interoperability, and analytics teams to ensure AI/ML deliverables remain cohesive with the overall platform design.

·   Document AI/ML assumptions, limitations, data requirements, model logic, and recommended next steps for future implementation.

·   Ensure AI/ML outputs are designed responsibly, with attention to data privacy, explainability, accuracy, and appropriate use.

·   Bachelor’s degree in Computer Science, Data Science, Engineering, Artificial Intelligence, Machine Learning, or a related field.

·   At least 4 to 6 years of experience in machine learning engineering, data science, MLOps, or AI system development.

·   Strong proficiency in Python for data manipulation, model development, and pipeline scripting, with solid working knowledge of SQL.

·   Experience working with data warehouses, data pipelines, ETL/ELT processes, or analytics-ready datasets.

·   Demonstrated experience developing, evaluating, and deploying machine learning models or AI-enabled tools in structured project environments.

·   Familiarity with ML frameworks such as Scikit-learn, TensorFlow, PyTorch, Hugging Face, or comparable tools.

·   Experience with MLOps tooling and practices such as Airflow, MLflow, CI/CD pipelines, model registries, version control, and monitoring.

·   Familiarity with cloud and containerised deployment environments including AWS, GCP, Azure, Docker, or Kubernetes.

·   Ability to communicate AI/ML methods, model limitations, and technical trade-offs clearly to non-technical stakeholders.

·   Strong documentation, collaboration, and analytical problem-solving skills.

·   Experience designing, testing, and deploying production-grade algorithms or AI/ML workflows in operational environments.

·   Hands-on experience with forecasting, anomaly detection, entity matching, recommendation systems, NLP, RAG, or LLM-based workflows.

·   Experience designing AI/ML solutions built on data warehouse or lakehouse architectures.

·   Advanced degree or ongoing postgraduate study in AI, Machine Learning, Data Science, Computer Science, or Engineering.

Palladium is committed to embedding equity, diversity, and inclusion into everything we do. We welcome applications from all sections of society and actively encourage diversity to drive innovation, creativity, success, and best practice.

We define Safeguarding as the preventative action taken by Palladium to protect our people, clients and the communities we work with from harm. All successful candidates will be subject to an enhanced selection process including safeguarding-focused interviews and a rigorous due diligence process.

 

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