Remote ML Platform Engineer | MLOps & Production AI

Simcon; United States; Remote; USD 120,000 - 190,000; Full time

Job summary

Cadmould seeks a platform engineer to build and run the training data and model lifecycle for the Cadmould AI Solver. You will create reproducible pipelines, manage data and model assets, and bring ML workflows into production on AWS.

You will monitor model performance and data drift, enabling AI engineers to iterate rapidly. The role emphasizes production-grade systems, collaboration with the Cloud team, and scale across multiple models with access controls and user-specific configurations.

Qualifications

• Background in Computer Science, Data Engineering, Machine Learning, or a related field, with 3+ years of industry or research experience.
• Strong Python skills and solid software engineering fundamentals (testing, version control, CI/CD).
• Experience bringing software services or data pipelines into production. ML systems in production is a plus.
• Experience with a cloud environment (AWS, Azure or GCP) and with containerization using Docker.
• Proactive, reliability-minded, and able to build scalable systems.
• Working in English; German a plus as the culture is shifting toward English.

Responsibilities

• Build training and data platform: versioning, tracking, and management of training data and models with reproducibility and lineage.
• Create the model lifecycle: versioning, a registry, promotion, and repeatable training and deployment.
• Set up monitoring for model degradation and data drift to guide AI engineers.
• Provide workflows and tooling for AI engineers and data scientists to train, evaluate, and deploy models.
• Operate and scale the AWS service that serves AI models, ensuring speed and reliability.
• Collaborate with the Cloud team to align platform capabilities with production needs.

Job description

Cadmould seeks a platform engineer to build and run the training data and model lifecycle for the Cadmould AI Solver. You will create reproducible pipelines, manage data and model assets, and bring ML workflows into production on AWS.

You will monitor model performance and data drift, enabling AI engineers to iterate rapidly. The role emphasizes production-grade systems, collaboration with the Cloud team, and scale across multiple models with access controls and user-specific configurations.