Job description

About DEUNA DEUNA is a payments infrastructure platform that helps enterprise merchants across Latin America, the US, and Europe optimize and orchestrate their entire payment stack. We combine payment routing intelligence, AI-driven optimization, and a composable checkout experience to help companies increase revenue and reduce operational complexity at scale. We are backed by leading investors and processing billions of dollars in annual transaction volume. About the Role DEUNA is a payments infrastructure company powering enterprise commerce across Latin America, the US, and Europe. We operate at the intersection of high-volume payment orchestration and applied AI — building intelligent systems that optimize authorization rates, reduce costs, and automate complex payment workflows for some of the largest merchants in the world. We are looking for a Staff/Principal-level AI Platform Tech Lead to own the full technical stack behind our AI payment intelligence and digital workforce products — from ML model training through production routing integration. This is a hands-on leadership role: you will set the architecture, write the code, and grow the team. What You Will Do ML & AI Systems Design, train, and own the full lifecycle of ML models for payment optimization — routing decisions, authorization rate improvement, cost reduction, and fraud signals — using PyTorch, TensorFlow, or XGBoost. Build and operate LLM-powered workflows: LangGraph agent orchestration, RAG pipelines, and vector DB integrations (Pinecone, pgvector, or Weaviate). Own the MLOps stack end-to-end: experiment tracking (MLflow / W&B), model registry, feature store, and automated retraining pipelines on AWS SageMaker. Monitor model health continuously — drift, distribution shifts, retraining triggers — and define evaluation metrics tied directly to business outcomes. Platform Engineering & Payments Integration Build and maintain inference services in Go and Python integrated into live payment routing — strict latency SLAs (<100 ms), zero silent errors. Own AWS infrastructure: ECS/EKS, Terraform IaC, SQS/SNS event streaming, RDS/Aurora, and S3 for model artifacts. Design and ship on-premise and hybrid deployment architectures for enterprise clients requiring local data residency, including secure data sync pipelines. Apply PCI-DSS standards across all components touching payment data; implement tokenization in ML pipelines; design for PSP-specific behavior (Cybersource, Worldpay, Prosa, Cielo, Pagbank, and others). Build and maintain RESTful and gRPC APIs that expose AI platform capabilities to merchants and partners. Technical Leadership Own observability end-to-end: Prometheus/Grafana dashboards, OpenTelemetry tracing, model-specific monitors, and on-call runbooks. Set the engineering bar for the team: architecture reviews, code standards, testing strategy (unit, integration, shadow mode), and CI/CD practices. Mentor engineers, run design reviews, and translate product vision into executable technical roadmaps with clear timelines and trade-offs. Technical Skills Backend / Platform Go (production services) Python (ML + tooling) gRPC & REST APIs Event streaming (SQS/SNS) Distributed systems Cloud & Infra — AWS ECS / EKS Terraform / IaC SageMaker or Vertex AI RDS/Aurora, S3 Hybrid / on-prem deploy AI / ML Stack PyTorch or TensorFlow XGBoost / scikit-learn MLflow / W&B Feature stores Model monitoring & drift LLMs & Agents LangGraph / LangChain RAG + vector DBs Prompt engineering LLM evaluation Structured outputs Payments Domain PCI-DSS compliance Tokenization patterns PSP integrations Auth rate optimization Routing orchestration Frontend React / Next.js TypeScript Component systems API integration Observability Prometheus / Grafana OpenTelemetry Structured logging On-call runbooks Data SQL (analytical) Airflow / dbt Feature pipelines Data quality & lineage What We Are Looking For 8+ years in software engineering; 3+ at Staff, Principal, or Tech Lead level owning a production platform end-to-end. Proven track record shipping ML/AI systems to production: training, serving, monitoring, and retraining — not just prototyping. Hands-on LLM experience in production: agents, RAG pipelines, or AI workflow orchestration. Payments or fintech background with practical knowledge of PSP behavior, PCI-DSS scope, authorization logic, and routing trade-offs. Experience designing and deploying on-premise or hybrid enterprise infrastructure. Bachelor's degree in Computer Science, Engineering, or equivalent demonstrated depth. What we offer A greenfield opportunity to define architecture, tooling, and engineering standards for an AI platform operating at scale across LatAm, US, and Europe. Ownership of one of the most technically complex and business-critical systems at DEUNA — from model training through live payment routing. Direct collaboration with product, operations, and modeling leadership — short feedback loops, high autonomy, real impact. Competitive compensation, hybrid work and a team that takes engineering craft seriously.

Requirements

Must-haves

ML & AI Systems
Design, train, and own the full lifecycle of ML models for payment optimization — routing decisions, authorization rate improvement, cost reduction, and fraud signals — using PyTorch, TensorFlow, or XGBoost.
Build and operate LLM-powered workflows: LangGraph agent orchestration, RAG pipelines, and vector DB integrations (Pinecone, pgvector, or Weaviate).
Own the MLOps stack end-to-end: experiment tracking (MLflow / W&B), model registry, feature store, and automated retraining pipelines on AWS SageMaker.
Monitor model health continuously — drift, distribution shifts, retraining triggers — and define evaluation metrics tied directly to business outcomes.
Platform Engineering & Payments Integration
Build and maintain inference services in Go and Python integrated into live payment routing — strict latency SLAs (<100 ms), zero silent errors.
Own AWS infrastructure: ECS/EKS, Terraform IaC, SQS/SNS event streaming, RDS/Aurora, and S3 for model artifacts.
Design and ship on-premise and hybrid deployment architectures for enterprise clients requiring local data residency, including secure data sync pipelines.
Apply PCI-DSS standards across all components touching payment data; implement tokenization in ML pipelines; design for PSP-specific behavior (Cybersource, Worldpay, Prosa, Cielo, Pagbank, and others).
Build and maintain RESTful and gRPC APIs that expose AI platform capabilities to merchants and partners.
Technical Leadership
Own observability end-to-end: Prometheus/Grafana dashboards, OpenTelemetry tracing, model-specific monitors, and on-call runbooks.
Set the engineering bar for the team: architecture reviews, code standards, testing strategy (unit, integration, shadow mode), and CI/CD practices.
Mentor engineers, run design reviews, and translate product vision into executable technical roadmaps with clear timelines and trade-offs.
Technical Skills
Backend / Platform
Go (production services)
Python (ML + tooling)
gRPC & REST APIs
Event streaming (SQS/SNS)
Distributed systems
Cloud & Infra — AWS
Terraform / IaC

Nice-to-haves

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Keywords to mirror

aimlllmawsgopythonci/cdrestreactnext.jstypescriptapisql

Platform: lever
Effort: quick

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