Flutterwave

AI Engineer

Flutterwave

full-time

Posted on:

Location Type: Remote

Location: United States

Visit company website

Explore more

✨ AI Apply
Apply

Tech Stack

DockerKubernetesPythonPyTorchTensorflow

About the role

  • 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
  • Ensure personal information of customers, employees, and other individuals processed and protected in line with applicable data privacy policies.

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
  • Strong preference for previous fintech or payments experience
Benefits
  • Health insurance
  • Flexible work arrangements
  • Professional development opportunities
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
Machine LearningAI EngineeringPythonPyTorchTensorFlowHugging FaceLLMsinference pipelinesevaluation metricsML governance
Soft Skills
problem-solvinganalytical thinkingcommunicationcollaborationattention to detailorganizational skillsefficiency improvementdocumentation