MLops Engineer

This ML Ops Engineer role focuses on building and maintaining the infrastructure for machine learning model deployment and monitoring. The position involves automating ML pipelines, managing model lifecycle, and ensuring production reliability.

Architects automated pipelines for the AI/ML infrastructure. Leads deployment and monitoring of Conversational AI, RAG, and LLM systems using Kubernetes, Docker, and Terraform.

This role focuses on the operational aspects of data and AI systems, including monitoring, maintenance, and optimization. It requires experience with DevOps practices and data pipelines to ensure reliable and efficient system performance.

This senior MLOps role focuses on operationalizing machine learning models, building and maintaining CI/CD pipelines for ML, and ensuring model monitoring and reliability. The engineer will work with data scientists to deploy and scale ML models in production environments.

This role focuses on building and scaling Seedtag's neuro-contextual advertising platform, leveraging proprietary AI and a contextual operating system. The engineer will tackle complex technical challenges in a high-growth, global environment, contributing to the core technology that powers privacy-first advertising.

This senior role leads the development of Seedtag's agentic platform, focusing on building scalable AI-driven systems for advertising. The engineer will provide technical leadership in a fully remote environment, driving innovation in neuro-contextual advertising technology.

This role focuses on developing Seedtag's agentic platform, building autonomous AI agents for advertising optimization. The engineer works on cutting-edge technology in a fully remote setting, contributing to the evolution of neuro-contextual advertising.