Machine Learning Operations Engineer II

CLA (CliftonLarsonAllen)•Atlanta, GA

21h

About The Position

CLA is a top 10 national professional services firm dedicated to creating opportunities for clients, people, and communities through various advisory, digital, audit, tax, consulting, and outsourcing services. CLA is growing and seeking to hire an experienced Machine Learning Operation Engineer to join their Data Science Team. This role offers growth, flexibility, and a collaborative work environment, providing an opportunity to learn, build, and grow alongside experienced developers.

Requirements

2+ years' experience as a Data/ML Engineer
Bachelor's degree is required. Combination of relevant experience, education, and training may be accepted in lieu of degree.
Familiarity with cloud platforms (e.g., AWS, Azure, GCP).
Basic understanding of SQL and experience with ETL pipeline design and maintenance.
Knowledge of CI/CD concepts and experience with tools like Jenkins, Git, Perforce, etc.
Proficiency in programming languages such as Python.
Knowledge of data pipeline architecture and data transformation.
Knowledge of machine learning model deployment and management.
Familiarity with performance monitoring and optimization techniques.
Basic understanding of machine learning and DevOps principles.

Responsibilities

Assist with design and development of MLOps infrastructure to support the deployment, management, and monitoring of machine learning models within the firm.
Collaborate with data scientists and engineers to ensure seamless integration of data science and engineering processes.
Support maintenance of automated workflows and pipelines for efficient model deployment and monitoring.
Troubleshoot and resolve any issues with model performance, data pipelines, or infrastructure.
Assist with ensuring the security and scalability of the MLOps infrastructure to handle large volumes of data and models.
Support maintenance of documentation for MLOps processes and systems.
Collaborate with cross-functional teams to understand business needs and translate them into technical requirements.
Ensure compliance with data security and privacy regulations.
Stay up to date with industry trends and advancements in machine learning and MLOps.