AI / ML Engineer

The client is an African nonprofit improving healthcare access. The role is for a Senior AI/ML Engineer to work on risk and fraud detection using LLM and agentic AI systems. The position is remote and requires strong Python and production ML experience.

• 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.
• 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.

• 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.