AI Engineer

Responsibilities

Productionize AI for Payment Incident Investigation

• 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).

Build Reusable Internal AI Infrastructure

• 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.

Expand AI into Operational Workflows

• 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.

Ensure AI Reliability & Governance

• 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.
• Ensure personal information of customers and employees is protected in line with applicable data privacy policies and laws.

Requirements

• 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.
• BA/BSc/HND degree.
• Strong preference for previous fintech or payments experience.

How to Apply

Interested and qualified candidates should apply by visiting the Flutterwave recruitment portal on BambooHR via the provided application link.