Core Responsibilities

1 · AI/ML Model Ownership

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  Design, train, and fine-tune ML models for payment optimization use cases — including authorization rate improvement, dynamic routing, cost minimization, and fraud signal detection.

  
  


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  Select and apply the right frameworks (PyTorch, TensorFlow, scikit-learn) per model type and latency budget.

  
  


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  Own the model lifecycle: experimentation → offline evaluation → shadow deployment → A/B testing → production promotion.

  
  


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  Monitor and remediate model drift, data distribution shifts, and performance degradation proactively.

  
  


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  Define evaluation metrics that map directly to business KPIs (approval rate lift, GMV impact, provider cost).

  
  

2 · Data Pipelines & Feature Engineering

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  Architect and build optimized data pipelines to collect, clean, and preprocess high-volume transaction data for model training and inference.

  
  


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  Design feature stores and real-time feature serving layers that keep inference latency within payments SLA requirements (<100 ms).

  
  


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  Establish data quality standards, schema validation, and lineage tracking across the ML data stack.

  
  


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  Partner with the Data Engineering team to ensure training data reflects the full distribution of providers, regions, and merchant types in our network.

  
  

3 · Production Deployment & Payments Stack Integration

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  Integrate ML model outputs into DEUNA's live payment routing and orchestration layer with zero tolerance for latency regressions or silent errors.

  
  


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  Develop and own the inference service layer in Go and Python, ensuring thread-safe, performant, and fault-tolerant operation under peak transaction load.

  
  


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  Lead the design of hybrid deployment architectures: cloud-native (AWS/GCP) and on-premise client environments, including secure bi-directional data synchronization.

  
  


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  Build and maintain RESTful and gRPC APIs that expose Athia capabilities to the broader DEUNA platform and external partners.

  
  

4 · Observability, Monitoring & Incident Response

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  Own the full observability stack for Athia: real-time dashboards, alerting thresholds, anomaly detection, and post-incident reviews.

  
  


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  Implement model-specific monitoring (prediction distributions, confidence scores, provider error rates) alongside standard infrastructure metrics.

  
  


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  Create a fast feedback loop with the Operations team to detect and remediate routing degradation or GMV impact within SLA.

  
  


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  Define on-call runbooks and escalation paths that are clear, tested, and kept up to date.

  
  

5 · Scalability, Resiliency & Engineering Leadership

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  Provide architectural guidance to scale Athia to handle 10M+ monthly transactions across concurrent global partner launches.

  
  


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  Lead and mentor engineers through architecture reviews, code reviews, technical planning, and day-to-day execution.

  
  


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  Drive engineering best practices: testing strategy (unit, integration, shadow), CI/CD pipelines, documentation standards, and security compliance.

  
  


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  Translate business and product goals into concrete technical roadmaps with realistic timelines and clear dependency mapping.

  
  

Requirements

Experience

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  6+ years in software engineering with strong backend foundations.

  
  


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  2+ years in a Tech Lead or Staff Engineer role owning a production platform end-to-end.

  
  


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  Demonstrated experience shipping ML/AI systems to production — not just research or notebooks.

  
  


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  Background in payments, fintech, or high-transaction environments strongly preferred.

  
  


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  Experience with on-premise deployment or hybrid infrastructure for enterprise clients is a plus.

  
  


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  Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.