Principal Generalist Engineer
AI in this role
Build and scale production systems and ML pipelines in a high-ownership R&D engineering role.
Who we are
Moniepoint Inc. is Africa’s all-in-one financial platform, helping 20 million businesses and individuals access seamless payments, banking, credit, cross-border, and business management tools each month.
As Nigeria’s largest merchant acquirer, we power most of the country’s point-of-sale (POS) transactions. Through our subsidiaries, Moniepoint Inc. processes over $250 billion in digital payment transaction value annually.
About the role
We are hiring a senior Generalist Engineer for our Research & Development Team who can own problems end to end and deliver complete production systems, not just components. This is a broad, high-ownership role for an engineer who can operate across software, distributed systems, infrastructure, data, and, where useful, machine learning, moving from vague problem statements to clear designs, robust implementations, reliable deployment, and effective operation in production.
Success in this role requires strong first-principles thinking, sound technical judgment, deep debugging ability, and the willingness to work across boundaries to get the job done. We are looking for a true engineering generalist: someone who reduces complexity, improves reliability and performance, and treats ML as one tool among many rather than a default answer.
Curious about what makes Moniepoint an incredible place to work? Check out posts on how we cultivate a culture of innovation, teamwork, and growth.
What You'll Be Doing
- Design and build production-grade systems that are reliable, scalable, and observable.
- Own systems end-to-end: problem → design → data → implementation → deployment → operations.
- Work across application services, distributed systems, infrastructure, data pipelines, and ML systems
- Debug complex production issues across multiple layers
- Make engineering trade-offs grounded in first principles
- Improve performance, latency, reliability, and cost efficiency
- Contribute to architecture and technical direction
- Write maintainable code and documentation
- Raise the engineering bar
- Machine Learning as Part of the Role
- Frame problems correctly: when to use ML vs deterministic systems
- Work with data end-to-end
- Train, evaluate, and iterate on models
- Build reproducible pipelines
- Deploy models and monitor performance, drift, and cost
- Debug system + model failures
What We're Looking For
- Strong CS fundamentals (DSA, OS, networking, distributed systems)
- Solid probability and statistics
- Experience building production systems at scale
- Ability to move across languages (Go, Java, Python, Rust, SQL)
- Understanding of system behavior under load and failure
- Comfort with Linux, containers, Kubernetes
- Strong debugging skills
- Ability to reason using invariants and failure modes
- Data systems, streaming systems, ML infrastructure, performance optimization
What We Value
- Ownership over problems
- Simplicity over complexity
- Curiosity about internals
- Robust design thinking
- Clear communication
What Success Looks Like
- Deliver working production systems from ambiguous problems
- Apply ML only when needed
- Build reliable, scalable systems
- Reduce complexity
- Earn trust in technical judgment
If you are a builder, a systems thinker, and a technologist who loves exploring what’s next, we’d love to have you help shape the future of Moniepoint.
What we can offer you
- Culture - We put our people first and prioritize the well-being of every team member. We’ve built a company where all opinions carry weight and where all voices are heard. We value and respect each other and always look out for one another. Above all, we are human.
- Learning - We have a learning and development-focused environment with an emphasis on knowledge sharing, training, and regular internal technical talks.
- Compensation - You’ll receive an attractive salary, pension, health insurance, paid leave plus other benefits.
What to expect in the hiring process
Our interview process is designed to be thoughtful, transparent, and candidate-friendly, allowing you to showcase your strengths while getting to know us better:
- Initial Conversation: A brief introductory call with our recruiter to learn more about your background, career goals, and to share insights about the role and our team.
- Technical Assessment: A practical, take-home assessment follow by a discussion with an interviewer on a call.
- System Design Interview: A focused session with our Engineering Team, where we'll explore system design, architecture, and problem-solving approaches.
- Final Interview: A combined technical and behavioural conversation with a member of our Executive Team, aimed at understanding your alignment with our values, vision, and culture
Moniepoint is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees and candidates.
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How we rate this
Principal Generalist Engineer at Moniepoint rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- Tell me about a project where distributed systems was part of your work. What did you do?
- Tell me about a project where machine learning was part of your work. What did you do?
- Tell me about a project where data pipelines was part of your work. What did you do?
- Tell me about a project where system architecture was part of your work. What did you do?
- Tell me about a project where debugging was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Distributed Systems, Machine Learning, Data Pipelines, System Architecture, and Debugging. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.
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