DEUNA
FinTech
AIPlatformTechLead
Neural analysis suggests this role is
optimal for Lead candidates.
“AI Platform Tech Lead at DEUNA. Skills: AI Platform, MLOps, Payments Infrastructure, LLMs. Design ML models for payment optimization. Train ML models for payment optimization”
What You'll Achieve.
Increase revenue; Reduce operational complexity; Optimize authorization rates; Reduce costs; Automate complex payment workflows
Industry & Context.
Root cause analysis
On-call runbooks
What They're Looking For.
Must Have
8+ years in software, 3+ at Staff, Principal, or Tech Lead level, Proven track record shipping ML/AI systems to production, Hands-on LLM experience in production, Payments or fintech background, Experience designing and deploying on-premise or hybrid enterprise infrastructure, Bachelor's degree in Computer Science, Engineering, or equivalent
Nice to Have
Staff/Principal-level AI Platform Tech Lead
What You'll Do.
Design ML models for payment optimization
Train ML models for payment optimization
Own ML model lifecycle
Build LLM-powered workflows
Operate LLM-powered workflows
Integrate RAG pipelines
Own MLOps stack end-to-end
Manage experiment tracking
Manage model registry
Manage automated retraining pipelines
Monitor model health continuously
Define evaluation metrics
Build inference services
Maintain inference services
Integrate inference services into payment routing
Own AWS infrastructure
Build AWS infrastructure
Maintain AWS infrastructure
Design on-premise deployment architectures
Ship on-premise deployment architectures
Design hybrid deployment architectures
Ship hybrid deployment architectures
Build secure data sync pipelines
Apply PCI-DSS standards
Implement tokenization
Maintain RESTful APIs
Own observability end-to-end
Set engineering bar for team
Conduct architecture reviews
Define code standards
Define testing strategy
Define CI/CD practices
Translate product vision into roadmaps
How You'll Work.
Team & Collaboration
Cross-functional teams; Product leadership; Operations leadership; Modeling leadership
Communication Scope
Technical roadmaps
Process & Methodology
Roadmap planning
Full Job Description
## 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
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