AI Engineering Lead

About the Role

Athia is DEUNA's AI-powered payment intelligence platform — moving from early ML experimentation to the critical infrastructure behind billions of dollars in annual transaction volume. We are looking for a hands-on Engineering Lead who can own the full technical stack: from model development and data pipelines to production payment orchestration, cloud/on-prem deployments, and real-time observability.

This is not a coordination role. You will build, ship, and own. You will be the technical authority that bridges AI/ML systems with our core payments stack, leading both the platform engineering and the modeling lifecycle end-to-end.

Core Responsibilities

1 · AI/ML Model Ownership

• Design, train, and fine-tune ML models for payment optimization use cases — including authorization rate improvement, dynamic routing, cost minimization, and fraud signal detection.
• Select and apply the right frameworks (PyTorch, TensorFlow, scikit-learn) per model type and latency budget.
• Own the model lifecycle: experimentation → offline evaluation → shadow deployment → A/B testing → production promotion.
• Monitor and remediate model drift, data distribution shifts, and performance degradation proactively.
• Define evaluation metrics that map directly to business KPIs (approval rate lift, GMV impact, provider cost).

2 · Data Pipelines & Feature Engineering

• Architect and build optimized data pipelines to collect, clean, and preprocess high-volume transaction data for model training and inference.
• Design feature stores and real-time feature serving layers that keep inference latency within payments SLA requirements (<100 ms).
• Establish data quality standards, schema validation, and lineage tracking across the ML data stack.
• 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

• Integrate ML model outputs into DEUNA's live payment routing and orchestration layer with zero tolerance for latency regressions or silent errors.
• Develop and own the inference service layer in Go and Python, ensuring thread-safe, performant, and fault-tolerant operation under peak transaction load.
• Lead the design of hybrid deployment architectures: cloud-native (AWS/GCP) and on-premise client environments, including secure bi-directional data synchronization.
• Build and maintain RESTful and gRPC APIs that expose Athia capabilities to the broader DEUNA platform and external partners.

4 · Observability, Monitoring & Incident Response

• Own the full observability stack for Athia: real-time dashboards, alerting thresholds, anomaly detection, and post-incident reviews.
• Implement model-specific monitoring (prediction distributions, confidence scores, provider error rates) alongside standard infrastructure metrics.
• Create a fast feedback loop with the Operations team to detect and remediate routing degradation or GMV impact within SLA.
• Define on-call runbooks and escalation paths that are clear, tested, and kept up to date.

5 · Scalability, Resiliency & Engineering Leadership

• Provide architectural guidance to scale Athia to handle 10M+ monthly transactions across concurrent global partner launches.
• Lead and mentor engineers through architecture reviews, code reviews, technical planning, and day-to-day execution.
• Drive engineering best practices: testing strategy (unit, integration, shadow), CI/CD pipelines, documentation standards, and security compliance.
• Translate business and product goals into concrete technical roadmaps with realistic timelines and clear dependency mapping.

Requirements

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| Backend & Infrastructure * Go (Golang) — production-grade services * Python — ML pipelines, model serving, tooling * RESTful APIs and gRPC * Distributed systems & event-driven arch * CI/CD, Docker, Kubernetes * Cloud platforms (AWS or GCP) * Hybrid / on-prem deployment patterns | AI / ML Stack * PyTorch or TensorFlow — training & fine-tuning * scikit-learn, XGBoost, or tabular ML * MLflow, Weights & Biases, or equivalent * Feature engineering & feature stores * Model monitoring & drift detection * A/B testing and shadow deployment * Low-latency inference architectures |

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| Frontend & Full-Stack * React and Next.js * TypeScript * Component design systems * API integration patterns | Observability & Data * Prometheus, Grafana, or Datadog * Structured logging & distributed tracing * SQL and analytical query patterns * Data pipeline tooling (Airflow, dbt, etc.) |

Experience

• 6+ years in software engineering with strong backend foundations.
• 2+ years in a Tech Lead or Staff Engineer role owning a production platform end-to-end.
• Demonstrated experience shipping ML/AI systems to production — not just research or notebooks.
• Background in payments, fintech, or high-transaction environments strongly preferred.
• Experience with on-premise deployment or hybrid infrastructure for enterprise clients is a plus.
• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.