Machine Learning Engineer

CLA is a top 10 national professional services firm where our purpose is to create opportunities every day, for our clients, our people, and our communities through industry-focused wealth advisory, digital, audit, tax, consulting, and outsourcing services. Even with more than 8,500 people, 130 U.S. locations, and a global reach, we promise to know you and help you.

CLA is dedicated to building a

culture

that invites different beliefs and perspectives to the table, so we can truly know and help our clients, communities, and each other.

Our Perks:

• Flexible PTO (designed to offer flexible time away for you!)
• Up to 12 weeks paid parental leave
• Paid Volunteer Time Off
• Mental health coverage
• Quarterly Wellness stipend
• Fertility benefits
• Complete list of benefits

here

CLA is growing and seeking to hire an experienced Machine Learning Operation Engineer: this position offers growth, flexibility and a collaborative work environment.

We’re looking for a motivated and curious Machine Learning Operation Engineer to join our Data Science Team. This is an opportunity to learn, build, and grow alongside experienced developers in a supportive, collaborative environment.

How you’ll create opportunities in this role:

• 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.

Technologies You'll Use

• 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.