Deuna
AI Platform Tech Lead
San Francisco
Sponsorship not specifiedDetected 60 days ago
TypeScriptPythonGoReactNext.jsDistributed SystemsSQLVector DatabasesAWSTerraformCI/CDPrometheusGrafanaPlatform EngineeringRESTgRPCMachine LearningTensorFlowPyTorchscikit-learnAirflowdbtLLMsRAG
About the role
- DEUNA is a payments infrastructure company powering enterprise commerce across Latin America, the US, and Europe.
- This is a hands-on leadership role: you will set the architecture, write the code, and grow the team.
- architecture reviews, code standards, testing strategy (unit, integration, shadow mode), and CI/CD practices.
Responsibilities
- 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.
- Build and maintain inference services in Go and Python integrated into live payment routing - strict latency SLAs (
- Own AWS infrastructure: ECS/EKS, Terraform IaC, SQS/SNS event streaming, RDS/Aurora, and S3 for model artifacts.
- 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.
Requirements
- 8+ years in software engineering
Skills
- Backend / Platform
- Go (production services)
- Python (ML + tooling)
- gRPC & REST APIs
- Event streaming (SQS/SNS)
- Distributed systems
- Cloud & Infra - AWS
- Terraform / IaC
- SageMaker or Vertex AI
- RDS/Aurora, S3
- Hybrid / on-prem deploy
- AI / ML Stack
Compensation
- We are backed by leading investors and processing billions of dollars in annual transaction volume.
Benefits
- Monitor model health continuously - drift, distribution shifts, retraining triggers - and define evaluation metrics tied directly to business outcomes.
- Mentor engineers, run design reviews, and translate product vision into executable technical roadmaps with clear timelines and trade-offs.
This listing is sourced directly from Deuna's careers page and normalized into a canonical job model.