• Review and refine AI-generated outputs related to machine learning operations, deployment pipelines, monitoring, and practical aspects of MLOps • Evaluate AI responses for technical accuracy, operational reliability, and compliance with real-world requirements • Draft realistic MLOps scenarios based on your direct professional experience • Create scenario variations from different perspectives (e.g. MLOps engineer, data scientist, developer, or product owner) • Identify gaps, oversights, or weak reasoning in AI-generated MLOps content

• Review and refine AI-generated outputs related to machine learning operations, deployment pipelines, monitoring, and practical aspects of MLOps • Evaluate AI responses for technical accuracy, operational reliability, and compliance with real-world requirements • Draft realistic MLOps scenarios based on your direct professional experience • Create scenario variations from different perspectives (e.g. MLOps engineer, data scientist, developer, or product owner) • Identify gaps, oversights, or weak reasoning in AI-generated MLOps content

- Design and ship a per-merchant payment volume forecasting system: time-series decomposition, Africa-specific event calendars (salary cycles, MNO maintenance windows, public holidays), quantile regression for uncertainty bounds - Build and maintain fraud/ anomaly detection across the payment stack (residual-based and model-driven) with tiered alerting logic mapped to merchant risk profiles. - Own the dynamic merchant segmentation system end-to-end: rule-based and data-driven hybrid, percentile thresholds grounded in EDA, segment-transition features as ML inputs - Instrument and monitor deployed models: drift detection, retraining triggers, and evaluation pipelines via Vertex AI - Build automation tooling that sits alongside the core ML work: Airflow DAGs, pipeline scaffolding, and tooling to reduce operational toil - Contribute to product and strategic thinking.

Role Description Kora is the marketplace for everything payments. We offer a robust payment API for payment collections, disbursements, and conversions for businesses anywhere in Africa. Our vision, which is at the core of what we do every day, is to create a world void of digital financial barriers. We are committed to delivering reliable, secure, and easy-to-use digital financial solutions to every single customer with a guarantee that it is improving their lives. To achieve this mission, we need people like you. We run payments across Africa and are now positioned as a global fiat and stablecoin payment infrastructure. We offer mobile money, virtual bank accounts, and virtual cards for payins and payouts across multiple markets. Our data infrastructure is batch-first (Airflow + a cloud data warehouse) and we use Vertex AI for our MLOps lifecycle. The ML team is high-ownership: you will build models, design systems, ship them, and observe them in production. You will work on merchant-facing intelligence: forecasting, anomaly detection, segmentation, as well as automation and product-layer ML. If you want to build practical things that matter in a context that most ML engineers never get near, this is the role. What You'll Work On - Design and ship a per-merchant payment volume forecasting system: time-series decomposition, Africa-specific event calendars (salary cycles, MNO maintenance windows, public holidays), quantile regression for uncertainty bounds. - Build and maintain fraud/anomaly detection across the payment stack (residual-based and model-driven) with tiered alerting logic mapped to merchant risk profiles. - Own the dynamic merchant segmentation system end-to-end: rule-based and data-driven hybrid, percentile thresholds grounded in EDA, segment-transition features as ML inputs. - Instrument and monitor deployed models: drift detection, retraining triggers, and evaluation pipelines via Vertex AI. - Build automation tooling that sits alongside the core ML work: Airflow DAGs, pipeline scaffolding, and tooling to reduce operational toil. - Contribute to product and strategic thinking. Our Stack - Apache Spark and Airflow - Google Vertex AI - Python - SQL - GCS/BigQuery Qualifications - 3+ years as an ML engineer in a production environment. - Strong Python and comfort with Spark for large-scale data processing. - Experience with time-series modelling: decomposition, forecasting, anomaly detection. - Solid grasp of the ML lifecycle as a unified discipline. - Ability to work with batch infrastructure and design for it deliberately. - High ownership mentality: you notice problems and fix them as opposed to waiting to be assigned. - Ability to identify gaps in data-driven business processes and come up with solutions. Strong Plus - Familiarity with Vertex AI (custom training jobs, model registry, pipelines, monitoring). - Experience in payments, fintech, or any domain where label quality, distribution shift, and operational constraints are real problems. - Exposure to African market dynamics. - n8n or similar automation/workflow tooling experience. Important You will be evaluated less on credentials or certifications and more on the quality of your thinking. In this team, a strong ML engineer: - Can explain why a design decision was made and what it trades off. - Writes systems that the next person can understand and build on. - Is honest about model limitations, especially in production contexts where overconfidence causes real loss. - Closes the loop between model outputs and business outcomes without needing to be told to. Benefits - Health insurance. - Sponsored and tailored training. - Paid parental leave. - Paid time-off. - Flexible work style. - Low-interest loans. - Group Life Insurance. - Access to up to four therapy sessions monthly. - Day off on your birthday 🎂 🎁 🎉. - Employee interest groups that provide supportive communities within Kora. - Great company culture and the opportunity to work with a highly collaborative team building something great! Note: We recognise imposter syndrome is real - any candidate who does not perfectly fit every characteristic of this role is still strongly encouraged to apply.