Senior Machine Learning Operations Engineer

CLA (CliftonLarsonAllen)•Seattle, WA

2d•$118,000 - $199,000

About The Position

CLA is a top 10 national professional services firm dedicated to creating opportunities for clients, employees, and communities. They are growing and seeking an experienced Senior Machine Learning Operations Engineer to join their Information Technology team. This role offers growth, flexibility, and a collaborative work environment. The ideal candidate will possess excellent interpersonal skills, strong problem-solving abilities, sound judgment, and a high level of integrity and dependability with a strong sense of urgency and results-orientation. This position leads the design, development, and deployment of advanced machine learning models and the supporting MLOps infrastructure.

Requirements

4 years relevant experience required
Experience in MLOps, DevOps, or related fields
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.

Responsibilities

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.