Lead ML Ops engineer

CLA (CliftonLarsonAllen)•Nashville, TN

About The Position

CLA is growing and seeking to hire an experienced Lead Machine Learning Operations Engineer to join our talented team. This role manages a team of Machine Learning Operations Engineers, oversees the end‑to‑end machine‑learning strategy and execution, sets vision for MLOps, and ensures alignment with business goals.

Requirements

6 years of relevant experience required.
Experience in MLOps, DevOps, or related fields, with a focus on enterprise-level solutions preferred.
Advanced proficiency in Python and architectural mastery of object‑oriented design across dynamically typed languages.
Broad experience integrating and governing multi‑language systems, including Python, JavaScript/TypeScript, and enterprise platforms (e.g., .NET).
Leadership‑level expertise in AI/ML platform engineering, spanning MLOps, LLMOps, and AIOps.
Ability to define and enforce enterprise standards for AI model lifecycle management, monitoring, reliability, and cost control.
Deep understanding of AI system observability, including drift detection, evaluation frameworks, and incident response.
Strong experience with cloud architecture, security, compliance, and enterprise‑scale deployments.
Proven ability to guide teams in technical decision‑making and platform strategy.

Nice To Haves

Supervisory experience preferred.
Degree in computer science, data science, or related field preferred.

Responsibilities

Define and execute an enterprise AI/ML platform strategy, encompassing MLOps, LLMOps, and AIOps, and build reusable frameworks and standards adopted across multiple projects and business units.
Oversee enterprise‑scale AI platforms supporting model training, inference, evaluation, monitoring, retraining, and governance, including generative AI systems.
Align AI and MLOps initiatives with business objectives, ensuring platforms and pipelines meet scalability, performance, security, regulatory, and cost requirements, including responsible and ethical AI considerations.
Implement and enforce best practices for model and prompt versioning, monitoring, retraining, and automated workflows, ensuring consistent and reliable AI operations.
Lead teams delivering shared AI infrastructure, tooling, and platforms, providing day‑to‑day leadership through coaching, development, and performance management.
Ensure platform reliability and operational excellence by overseeing escalated issue resolution, maintaining high‑quality documentation, and driving continuous improvement.
Track and evaluate industry trends in AI platforms, LLM ecosystems, and AI operations, translating insights into roadmap decisions and platform evolution.