Senior Machine Learning/AI Operations Engineer

As a Senior Machine Learning/AI Operations Engineer, you will develop and operate the infrastructure, tooling, and workflows that enable effective use of AI and Machine Learning at CMT. Your responsibilities will span operationalizing AI developer tools and model services, automating model workflows, and improving reliability, observability, and cost efficiency. You will partner with Engineering, Machine Learning, Compliance, Security Engineering, and SRE to translate their requirements into operational capabilities and controls. You will apply reproducible workflows, evaluation, controlled deployment, monitoring, and incident response to existing developer harnesses and coding tools, large language model (LLM) applications and agents, and machine learning training and inference. Current priorities emphasize AI usage across engineering teams, with shared operational ownership of model development and release infrastructure. The role requires hands-on expertise in scalable AI/ML systems, independent technical judgment, and the ability to turn operational needs into reusable capabilities. CMT is looking for a collaborative, customer-committed, and creative Senior Machine Learning/AI Operations Engineer who wants to join us in making roads safer by making drivers better! Responsibilities: Develop and optimize scalable AI/ML infrastructure, ensuring a balance between cost-efficiency and speed, with the goal of empowering Engineering and Machine Learning teams Operationalize AI developer harnesses, coding tools, and shared model services by tuning model configurations, integrating tools into engineering workflows, evaluating their effectiveness, and implementing usage controls agreed with Compliance and Security Engineering Optimize model access and inference through routing, rate limits, retries, timeouts, and fallbacks. Monitor token usage, latency, efficiency, throughput, and costs. Attribute usage and costs by team and workload, and implement budgets, quotas, and capacity controls Establish monitoring infrastructure for evaluating the performance of AI applications and machine learning models at scale. Trace model calls, retrieval, tool execution, and training workflows; monitor data quality, model drift, and output quality; and implement safeguards for sensitive data and tool access Operationalize evaluation workflows for models, prompts, and AI tool configurations. Work with Engineering and Machine Learning teams to track quality metrics and detect regressions, and with Compliance, Security Engineering, and SRE to incorporate policy, security, and reliability requirements into evaluation workflows and deployment decisions Develop and manage reproducible agent, training, and evaluation workflows, including permissions, execution limits, failure recovery, and traceability of inputs and outputs