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We are looking for a highly skilled Senior Engineer – MLOps to build, deploy, and operate scalable, reliable AI/ML infrastructure that powers our next-generation AI platforms. This role sits at the intersection of Machine Learning, DevOps, and Cloud Engineering, with a strong focus on supporting LLM agent systems, data pipelines, cloud infrastructure, and production AI workloads. The ideal candidate will have hands-on experience deploying and managing machine learning systems in cloud environments, automating infrastructure, building CI/CD pipelines, and ensuring the performance, scalability, and reliability of AI/ML platforms. WHAT YOU'LL DO Design, deploy, and manage scalable AI/ML infrastructure for production environments Build and maintain infrastructure supporting LLM agent systems, machine learning models, and data pipelines Deploy and operate ML/AI workloads across major cloud platforms such as AWS, GCP, or Azure Develop and maintain containerized applications using Docker and serverless container platforms such as Cloud Run, ECS Fargate, or Azure Container Apps Design and manage cloud databases, data warehouses, and storage solutions for AI applications Build and optimize ETL/ELT pipelines to support machine learning workflows and analytics Implement Infrastructure as Code (IaC) using Terraform or similar tools Design and maintain CI/CD pipelines for automated model deployment and infrastructure provisioning Implement monitoring, logging, alerting, and performance optimization for AI/ML systems Manage event-driven architectures using messaging platforms such as Kafka, Pub/Sub, SNS/SQS, or similar technologies Collaborate closely with AI/ML Engineers, Data Scientists, and Software Engineers to support model deployment and production operations Optimize infrastructure for reliability, scalability, latency, and cost efficiency Ensure security, governance, and operational best practices across cloud environments WHAT YOU'LL NEED Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field 6–10 years of experience in MLOps, DevOps, Cloud Engineering, or Infrastructure Engineering Proven experience deploying and operating Machine Learning or AI systems in production Strong expertise in cloud platforms including Google Cloud Platform (GCP), Amazon Web Services (AWS), or Microsoft Azure Experience with Infrastructure as Code (Terraform preferred) Strong Python programming skills Excellent analytical, troubleshooting, and problem-solving skills Strong communication skills with the ability to collaborate across technical and non-technical teams Expert-level Python or Typescript Very deep experience with at least one major cloud platform, including microservice creation and maintenance, databases and warehousing, and messaging/streaming infrastructure  Strong experience with containerization Strong experience with building-out telemetry and monitoring platforms Strong experience with ML model serving Expertise with infrastructure as code (IaC) such as Terraform Experience building and maintaining CI/CD pipelines Experience both implementing and advocating for MLOps best practices, model lifecycle management, and AI platform operations Knowledge of Vector Databases, Agent Orchestration and Cost Optimization NICE TO HAVE Experience with LLM Agent Frameworks such as Google ADK, LangChain, LangGraph, or similar Experience operating LLM/Generative AI workloads in production Experience supporting enterprise-scale AI/ML platforms Passion for building reliable, scalable, and automated infrastructure Ability to troubleshoot complex distributed systems Strong collaboration and stakeholder management skills A continuous learning mindset and enthusiasm for emerging AI technologies ABOUT US Born in 2001, Code and Theory is a digital-first creative agency t