Arionkoder - Sr AI Engineer (ML & Data)

Remote - LATAM15h ago

Remote Senior LATAM Cloud Computing Artificial Intelligence AI Engineer Documentation Pipeline Management AWS Azure Databricks Python Terraform MLOps MLflow

Requirements

• Machine learning or AI engineering background. • A track record of shipping agents and AI features to production, with users who rely on them today. • Hands-on experience with Databricks, developing and managing batch and streaming pipelines. • Cloud experience in Azure or AWS. • You use evals and data to decide when an agent is ready to ship and to measure whether it’s succeeding once live. • Strong Python, with experience building with LLM APIs and MCP. • Experience with agent frameworks or SDKs, and knowing when not to use one. • You already use coding agents as your main way of building software. • You work independently, gather requirements from non-engineering stakeholders and give specific answers. When you don’t know something, you say so. • Advanced English, written and oral. • Owning deployment of what you build: CI/CD, infrastructure as code (Terraform) and monitoring. • Processing complex documents with LLMs (OCR, extraction). • MLOps tools such as MLflow. • Background in logistics, transportation or compliance. • Remote, LATAM · Overlap with 9am–5pm US Eastern • Interviews are hands-on: expect to walk through something you’ve built and shipped, in detail.

Responsibilities

• Design, build and deploy AI agents end to end, from first spec to production. • Ship early and iterate on feedback from real users. • Work with product, support and engineering stakeholders to turn loose problems into clear requirements. • Build agents and AI features on top of the client’s data in Databricks. • Own what you ship once it’s live: monitoring, evals, cost and failure handling. • Help the team move faster by bringing in and spreading best practices in agentic engineering. • How you’ll work: • How you’ll work: • Use coding agents as your main way of building software: define the work, check what agents produce and make the calls that matter. • Break work into pieces an agent can finish and you can verify, and run several agents in parallel. • Rely on tests, CI and agent review so speed doesn’t cost quality. • When something goes wrong, improve your setup so it doesn’t happen again, and package what works as reusable skills for the team.

Benefits

• 💵 Monthly USD Service Fee🌴 20 business days of paid Agreed Time Off—Annual + 6 Local Holidays🐣Caregiver Time Off💻A dynamic remote work culture. You can work from anywhere!🚀An entrepreneurial environment.