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

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

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.