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# Senior AI/ML Engineer. Payments & Identity

- **Company:** Aya Global LLC
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Fraud Prevention and Detection, Python (Programming Language), Machine Learning, Language Modeling, Performance Tuning, Tensorflow, Pytorch, ONNX (Open Neural Network Exchange) Format, Power Analysis (Cryptography), Machine Learning Operations, TensorRT, Software Version Control, Data Pipelines
- **Published:** September 21, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=b73274c8a2c23190

## About the Role

* 5+ years applied ML/CV in production systems, preferably in identity, fraud, or real-time payments scoring
 * Strong Python engineering; production-grade experience with PyTorch or TensorFlow and their serving ecosystems (TorchServe, TF Serving, Triton Inference Server)
 * Deep understanding of face recognition architectures (ArcFace, CosFace, AdaFace) and PAD/liveness detection techniques
 * Experience designing and analysing A/B experiments at scale; familiarity with statistical methods (power analysis, CUPED, sequential testing)
 * MLOps fluency: model versioning (MLflow, Weights & Biases), feature stores, data pipelines, model monitoring and drift detection in production
 * Comfort with multi-modal learning: combining vision, NLP, and tabular signals in unified architectures
 * Bonus: experience fine-tuning or distilling small language models (SLMs); knowledge of LoRA, QLoRA, or RLHF pipelines

## Description

You will lead applied research and production ML at the core of Aye's identity and payments intelligence stack. This spans real-time biometric inference, fraud signal modelling, consumer behaviour learning, and multi-modal feature fusion, all under strict latency and privacy constraints. A defining part of the role is owning the A/B experimentation infrastructure that continuously validates model improvements in production, and contributing to the development of Aye's small language model (SLM) for in-store personalisation and merchant intelligence.

What You'll Build

 * Train, optimise, and serve face recognition, liveness detection (active and passive), anti-spoofing, and presentation attack detection (PAD) models at sub-300ms p99 latency in production
 * Design multi-modal inference pipelines that fuse biometric embeddings, transactional sequences, behavioural signals, and contextual metadata to power fraud detection and personalisation
 * Build and operate A/B and multi-armed bandit experimentation frameworks in production; instrument feature flags, holdout groups, and statistical significance pipelines to drive continuous model improvement
 * Develop and iterate on consumer behaviour models: purchase propensity scoring, cross-vertical affinity mapping, churn prediction, and next-best-offer recommendation for real-time CRM
 * Contribute to Aye's SLM initiative: fine-tune compact transformer models on anonymised retail interaction data for in-context merchant intelligence and consumer preference inference
 * Optimise models for edge deployment using TensorRT, ONNX Runtime, and quantisation techniques (INT8, FP16) on NVIDIA Jetson and OpenVINO-compatible hardware
 * Apply privacy-preserving ML techniques: federated learning, differential privacy, secure aggregation: to meet data residency and regulatory requirements across APAC jurisdictions