AI Engineer

Required competency and skillset to be a waver

• 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
  

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, employees, and other individuals the company conducts business with is processed and protected in line with applicable data privacy policies, privacy laws, and global best practices.