Full-Stack Support Engineer for AI Deployments

Span; Municipio de Esquel; On-site; ARS 97,133,805 - 104,605,636; Full time

Job summary

Span is seeking a Support Engineer to lead complex implementations with customers, embedding with engineering leaders to map workflows and deliver measurable improvements in developer productivity and organizational clarity. You will work at the intersection of product, engineering, and customer success to build full-stack solutions and ensure value in production environments.

You will be the voice of the customer, translating field learnings into product improvements and reusable patterns that

Qualifications

• 3+ years of engineering experience building full-stack products or complex integrations.
• Production-quality code in TypeScript/JavaScript and Python.
• Experience with data pipelines, ETL processes and LLM integration.
• Strong communication with technical and non-technical stakeholders.
• English communication to collaborate with US-based customers.

Responsibilities

• Own technical delivery across multiple customer deployments from initial prototype to production scale.
• Write production-grade code across our TypeScript, Python, and data pipeline stack.
• Translate customer feedback into product requirements and create reusable patterns that help the team scale.

Skills

Full-stack development

TypeScript/JavaScript

Python

Data pipelines

ETL processes

LLM integration

Stakeholder communication

English communication

Tools

TypeScript

NestJS

React

PostgreSQL

Redis

Airbyte

Dagster

Kafka

AWS

Kubernetes

FluxCD

Job description

Span is seeking a Support Engineer to lead complex implementations with customers, embedding with engineering leaders to map workflows and deliver measurable improvements in developer productivity and organizational clarity. You will work at the intersection of product, engineering, and customer success to build full-stack solutions and ensure value in production environments.

You will be the voice of the customer, translating field learnings into product improvements and reusable patterns that