Remote
At CloudGeometry, we’re redefining how modern data and AI systems are built. As a leading cloud-native engineering firm, we work with pioneering technology companies to deliver high-impact solutions across infrastructure, machine learning, and intelligent applications.
We are looking for a highly skilled AI Infrastructure Engineers x 5 people to join our growing team supporting large-scale AI/ML systems. This is a hands-on engineering role focused on building scalable, secure, and production-ready infrastructure that powers ML workflows end-to-end—from experimentation to deployment and monitoring.

What You’ll Do

• Design, implement, and maintain robust infrastructure for ML workflows across real-time and batch environments.
• Build and support production-grade model lifecycle systems, including registration, versioning, and deployment workflows.
• Develop APIs and backend services in TypeScript and Python to support model integration and orchestration.
• Manage and optimize infrastructure using AWS and infrastructure-as-code (CDK preferred).
• Work with Databricks MLFlow for end-to-end model management, including asset bundling and serving pipelines.
• Collaborate with cross-functional teams including ML scientists, backend engineers, and DevOps to deliver high-impact features.
• Monitor and improve infrastructure reliability, security, and performance across diverse deployment targets.
• Contribute to CI/CD workflows, container orchestration (Docker, ECS), and automation for ML pipelines.

Why Join CloudGeometry?
You’ll work alongside top-tier engineers across the US, LATAM, and Europe on cutting-edge projects in AI, cloud, and enterprise SaaS. We value deep technical curiosity, strong collaboration, and a bias for action in solving meaningful problems.
-

SKILLS

• Large Language Models (LLM)

• Software as a Service (SaaS)

• Databricks Products
• Python (Programming Language)
• Infrastructure
• TypeScript
• MLflow
• MLOps
• Amazon Web Services (AWS)

REQUIREMENTS

What We’re Looking For

• 7+ years in software or infrastructure engineering with proven experience supporting AI/ML systems.
• Deep hands-on experience with AWS services and modern IaC practices (Terraform/CDK).
• Strong backend programming skills in TypeScript and Python.
• Production-level use of MLFlow for model management and deployment.
• Expertise in containerization (Docker), CI/CD automation, and orchestration tools.
• Solid understanding of designing scalable and secure systems in cloud-native environments.
• Strong communication skills, able to bridge gaps between engineering and product stakeholders.
• Comfortable in fast-paced, collaborative environments working across time zones.

Nice to Have

• Exposure to LLM infrastructure and frameworks (e.g., DSPy, LangChain).
• Knowledge of LLM performance metrics: latency, cost monitoring, and usage optimization.
• Familiarity with semantic search tools and vector stores (e.g., OpenSearch, Pinecone).

• Design, implement, and maintain robust infrastructure for ML workflows across real-time and batch environments.
• Build and support production-grade model lifecycle systems, including registration, versioning, and deployment workflows.
• Develop APIs and backend services in TypeScript and Python to support model integration and orchestration.
• Manage and optimize infrastructure using AWS and infrastructure-as-code (CDK preferred).
• Work with Databricks MLFlow for end-to-end model management, including asset bundling and serving pipelines.
• Collaborate with cross-functional teams including ML scientists, backend engineers, and DevOps to deliver high-impact features.
• Monitor and improve infrastructure reliability, security, and performance across diverse deployment targets.
• Contribute to CI/CD workflows, container orchestration (Docker, ECS), and automation for ML pipelines