AI Engineer at Flutterwave
Flutterwave View all jobs
- Kenya
- Permanent
- Full-time
- Design and deploy a locally hosted LLM-powered agent for autonomous payment failure analysis.
- Build internal LLM infrastructure with no external API dependency for core workflows.
- Develop structured pipelines for root cause identification across transaction failures.
- Automate Level 1 incident investigations.
- Generate standardized root cause analysis (RCA) reports.
- Optimize model performance to reduce Mean Time to Resolution (MTTR).
- Develop scalable training and inference pipelines.
- Create reusable model components adopted across multiple use cases.
- Reduce time-to-deploy new AI applications.
- Decrease reliance on external AI APIs through internal model development.
- Implement monitoring systems for latency, drift, and model performance.
- Deploy at least two additional AI use cases (e.g., chatbot, automated reporting, issue clustering).
- Ensure ≥99.9% production uptime.
- Maintain inference latency within defined thresholds.
- Establish retraining cadence and continuous performance evaluation.
- Deliver measurable efficiency improvements in operational workflows.
- Implement version-controlled datasets and model versioning.
- Define evaluation benchmarks (precision, recall, accuracy thresholds).
- Implement automated drift detection.
- Document model architecture and training processes.
- Ensure zero preventable production-critical failures due to model design.
- 4-7+ years in Machine Learning / AI Engineering.
- Strong Python proficiency (PyTorch, TensorFlow, Hugging Face).
- Experience working with LLMs (fine-tuning, RAG, embeddings, retrieval systems).
- Experience deploying ML systems in production (Docker, Kubernetes, CI/CD).
- Experience building inference pipelines and monitoring systems.
- Strong understanding of evaluation metrics and ML governance practices.
- Experience working with large-scale structured and unstructured datasets.
- Strong preference for previous fintech or payments experience
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