DEUNA

FinTech

AIPlatformTechLead

$350–550k ~AI est. San Francisco, California, United States FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Lead candidates.

The Brief

“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.

FinTech
Problems you'll solve

Root cause analysis

Eligibility Requirements

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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