AI / ML Engineer

On behalf of our Client, Mobilunity is looking for a Senior AI / ML Engineer.

Our client is a high-growth, award-winning African nonprofit on a mission to radically transform healthcare access. The organization works to strengthen the potential and resilience of adolescent girls across Africa by building local health ecosystems that provide stigma-free, no-cost, quality-assured healthcare services.

Their technology platform connects community organizations, healthcare providers, and retail partners by enabling referrals, verifying service delivery, facilitating payments, and generating real-time data.

The organization operates across Kenya, Ethiopia, Uganda, Burkina Faso, South Africa, and Nigeria, with additional offices in Portugal, the Netherlands, and the United Kingdom. The global team consists of more than 250 international colleagues and works in a fast-paced, informal, and collaborative environment.

The role focuses on one of the organization’s critical scaling challenges: risk and fraud on the platform. It combines strong software engineering and data infrastructure skills with deep hands-on experience building and deploying modern AI and LLM-based systems in production.

Key Responsibilities:

Agentic Systems & GenAI Engineering

• Design, deploy, and debug sophisticated agentic AI workflows with multi-step reasoning, memory integration, and Model Context Protocol (MCP).
• Build and operate multi-agent systems using LangGraph, LangChain, or equivalent frameworks.
• Architect and continuously improve Retrieval-Augmented Generation (RAG) pipelines, including chunking strategies, retrieval mechanisms, and end-to-end evaluation.
• Deploy and operate LLM-based services in production.
• Optimise LLM systems for latency, throughput, reliability, and cloud costs using technologies such as LiteLLM, vLLM, SageMaker, or Bedrock.

Production ML & Engineering

• Develop traditional machine learning models for structured data, including classification, regression, ranking, and/or NLP use cases.
• Write clean, maintainable, production-quality Python code that integrates with the wider data and software engineering ecosystem.
• Work with large-scale structured datasets using complex SQL queries and data pipelines.
• Design and maintain evaluation and monitoring approaches for AI/ML systems.
• Contribute to team rituals, technical documentation, and shared engineering practices.
• Build systems with appropriate failure handling, monitoring, and graceful degradation.
• Take ownership of solutions from initial design through production operation and continuous optimisation.

Requirements:

• 5+ years of professional experience as an ML Engineer, Data Scientist, AI Engineer, Software Developer, or a similar role.
• Proven experience shipping and operating production software.
• Outstanding proficiency in Python.
• Strong experience working with large-scale structured data using SQL.
• Proven practical experience building and serving LLM-based or agentic AI systems for real users in production, rather than only developing internal prototypes.
• Hands-on experience with agent orchestration, multi-step AI workflows, or similar production GenAI systems.
• Solid understanding of traditional machine learning, with strong expertise in at least one area such as tabular ML, NLP, or deep learning.
• Strong understanding of production ML systems, including reliability, monitoring, evaluation, and optimisation.
• Strong verbal and written English communication skills.
• Ability to work independently, move quickly through iterative feedback loops, and take ownership of business outcomes.

Nice to Have:

• Experience in Risk, Fraud, Credit, Insurance, Cybersecurity, Fintech, or similar domains.
• Experience with GenAI observability, drift detection, and evaluation frameworks such as LangSmith, Evidently, or RAGAS.
• Hands-on experience with AWS environments and MLOps automation.
• Experience with containerisation and CI/CD for machine learning systems.
• Experience with LLM serving and optimisation using tools such as LiteLLM, vLLM, SageMaker, or Bedrock.
• Experience with LangGraph, LangChain, MCP, or multi-agent frameworks.
• Experience mentoring junior engineers or contributing to the wider technical community through blogging, talks, or public speaking.