Engineering

AI / ML Engineer

Lagos · Nigeria · Hybrid / remote (global clients) · Full-time

Experience requirements for this role are set in the required qualifications below—some roles ask for more than three years (for example sales leadership). Compensation and benefits are discussed with shortlisted candidates only—we do not publish salary ranges on public postings.

Own model development and production integration for client programs: from problem framing and datasets to evaluation, deployment, and monitoring—with clear documentation for regulated environments.

What you will do

• Partner with product and client stakeholders to translate business outcomes into measurable ML objectives and evaluation metrics
• Design and run experiments; document methodology, limitations, and failure modes for peer and client review
• Build and maintain training and inference pipelines with versioning, reproducibility, and cost awareness
• Collaborate with backend and data engineers on feature stores, batch/stream scoring, and API contracts
• Implement monitoring for drift, data quality, and model performance; participate in incident review
• Apply privacy, security, and ethics considerations appropriate to the client’s sector (e.g. financial services, health)
• Contribute to code review, shared libraries, and internal playbooks for responsible AI delivery

Required qualifications

• Minimum 3 years of professional software engineering experience, including substantial hands-on ML or applied AI delivery
• Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or equivalent practical experience
• Strong Python; proficiency with at least one major ML framework (e.g. PyTorch, TensorFlow, scikit-learn stack)
• Experience with data preparation, feature engineering, and offline/online evaluation—not only notebook prototypes
• Familiarity with cloud ML workflows (training jobs, object storage, secrets) on at least one major provider
• Solid written English for design notes, runbooks, and client-facing summaries
• Ability to work with distributed teams and overlapping time zones with clients

Preferred qualifications

• M.Sc. or Ph.D. in a quantitative field, or published work in relevant venues
• Experience with LLM/RAG pipelines, vector stores, or agentic workflows in production constraints
• Exposure to regulated industries and model risk or validation expectations
• Contributions to open source, internal platforms, or reusable MLOps tooling