AI Operations Specialist (MLOps)

TL;DR

ML Ops Engineer (AI): Design and maintain CI/CD pipelines for ML model training, deployment, and data pipelines across microservices with an accent on AWS ECS, Terraform automation, and LLM integration. Focus on scaling services, observability with Datadog and OpenTelemetry, and productionizing models using BentoML and FastAPI.

Remote: Argentina, Brazil, Chile, Colombia, Costa Rica, Mexico

Company

Leading education technology company delivering AI-powered solutions for K–12 classrooms, including content, assessment, and analytics grounded in learning science.

What you will do

• Design and maintain CI/CD pipelines for ML model training, packaging, and deployment across microservices.
• Manage containerized services on AWS ECS, optimizing for cost, latency, and availability.
• Automate infrastructure provisioning and service configuration with Terraform.
• Maintain and scale services using third-party LLM providers.
• Build and improve data pipelines from BigQuery, S3, and DynamoDB into training and inference workflows.
• Instrument services with observability tooling (Datadog, OpenTelemetry, Langfuse) and establish SLOs for model-serving endpoints.
• Collaborate with ML engineers to productionize new models using BentoML, FastAPI, and container-based serving.

Requirements

• 2-3 years in ML Ops supporting ML/AI features, systems, and workflows with 3-4 years prior experience in DevOps, CloudOps, or SRE
• Strong proficiency in Python.
• Hands-on experience with Docker containerization and container orchestration.
• Solid understanding of CI/CD for ML workflows in enterprise production.
• Experience with Infrastructure as Code, preferably Terraform.
• Familiarity with AWS (ECS, ECR, S3, DynamoDB, CloudWatch) and GCP (BigQuery, Vertex AI).
• Experience with LLM integration and observability (OpenAI API, Google GenAI, Langfuse).