- 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.

• As an AI / ML Engineer, you will be part of multidisciplinary teams delivering advanced analytics, machine learning, and AI-enabled solutions for federal clients. • You will design, build, and deploy scalable ML models and data pipelines that support mission-critical decision-making across defense and federal financial domains. • You will work closely with clients, data engineers, product owners, and functional SMEs to translate complex data and business problems into production-ready AI and ML solutions, while ensuring compliance with federal security and data-handling requirements. • Design, build, train, and deploy machine learning models to support operational, analytical, and decision-support use cases. • Develop and maintain end-to-end ML pipelines, from data ingestion and feature engineering through model training and evaluation. • Apply supervised and unsupervised learning techniques, including classification, regression, clustering, and anomaly detection. • Work with large-scale structured and semi-structured federal datasets, including financial, budgetary, and transactional data. • Engineer solutions in secure cloud and on-prem environments in compliance with DoD and federal security controls. • Collaborate with stakeholders to translate analytic outcomes into actionable insights and mission value. • Contribute to solution documentation, model explainability, and government-facing deliverables. • Support continuous improvement of data science and ML engineering best practices across teams.

• design, prototype, implement, and maintain scalable AI systems powered by LLMs and Foundation Models, • build agentic AI applications — multi-step LLM workflows with RAG, tool/function calling, and structured outputs, • design and run evaluation pipelines to measure and improve agent quality (LLM-as-judge, regression testing, safety/red-team evaluation), • collaborate with frontend, product, and other engineering teams to design API contracts and deliver end-to-end features, • monitor the performance, latency, cost, and SLAs of AI systems in production, • provide technical leadership and drive the necessary infrastructure or architectural changes.