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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Operations Engineer - **Company:** CliftonLarsonAllen LLP - **Location:** Seattle, WA, United States - **Experience:** Expert - **Salary:** $118,000.0 - $199,000.0 - **Contract:** Permanent contract - **Skills:** JavaScript (Programming Language), .NET Framework, Artificial Intelligence, Continuous Integration, DevOps, Distributed Systems, Python (Programming Language), Machine Learning, Object-Oriented Software Development, Performance Tuning, Azure Machine Learning, Software Construction, Strategies of Testing, TypeScript, Management of Software Versions, Large Language Models, Reliability of Systems, AI Platforms, Machine Learning Operations, Data Pipelines, Programming Languages - **Published:** August 1, 2026 - **Apply:** https://jobs.localjobnetwork.com/apply/add/87912707/1 ## About the Role In this position you should have the following; excellent interpersonal skills with the ability to communicate at all levels. Strong problem solving and creative skills and the ability to exercise sound judgment. Most important, demonstrate a high level of integrity and dependability with a strong sense of urgency and results-orientation., * 4 years relevant experience required + Experience in MLOps, DevOps, or related fields * Bachelor's degree or a combination of relevant experience and training may be considered in lieu of a degree. Technical Competencies: * Advanced proficiency in Python and strong command of object-oriented design in dynamically typed languages. Proven experience designing and maintaining systems using multiple programming languages (e.g., Python, JavaScript/TypeScript, .NET) within complex platforms. * Deep hands-on experience with AI/ML platform operations, supporting ML models, LLMs, NLMs, and SLMs in production. Strong expertise in MLOps and LLMOps, including model and prompt versioning, evaluation, monitoring, retraining, and governance. * Ability to design and optimize scalable inference architectures (batch, real-time, and event-driven). * Strong understanding of software engineering best practices, testing strategies, CI/CD, and system reliability. * Advanced experience with cloud platforms, distributed systems, and performance optimization. ## Description This role leads the design, development, and deployment of advanced machine learning models and the supporting MLOps infrastructure., * Lead the design and implementation of AI/ML platform and MLOps infrastructure, enabling deployment, management, monitoring, and governance of ML models, LLMs, NLMs, and SLMs in production environments. * Collaborate cross-functionally to integrate AI and machine learning capabilities into production systems, translating business requirements into scalable technical solutions. * Implement and enforce best practices across MLOps and LLMOps, including model and prompt versioning, feature management, monitoring, evaluation, retraining, and governance. * Design and operate reliable inference and orchestration patterns for AI systems, supporting batch, real-time, and event-driven workloads. * Troubleshoot and resolve complex issues across models, AI services, data pipelines, and infrastructure, ensuring reliability, security, scalability, and performance at scale. * Create and maintain technical and operational documentation, support escalations, and mentor junior engineers to raise team capability and consistency. * Evaluate emerging AI platform technologies, tools, and frameworks, guiding adoption aligned with business needs and platform strategy. ## Related Videos - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Do TypeScript without TypeScript](https://www.wearedevelopers.com/videos/327-do-typescript-without-typescript) - [This App Reached 10,000 Users in One Week. Here's How.](https://www.wearedevelopers.com/videos/100329-this-app-reached-10-000-users-in-one-week-here-s-how) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) - [DevOps Maturity Check – a way to balance autonomy and alignment](https://www.wearedevelopers.com/videos/58-devops-maturity-check-a-way-to-balance-autonomy-and-alignment) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)