MLOps Engineer

Arbitration Forums Inc.•Tampa, FL

8h•Remote

About The Position

This role at Arbitration Forums is as unique as it is rewarding because of the AF IPAAL Values (Integrity, Passion, Accountability, Achievement, Leadership) and TRI Model (Trust, Respect, Inclusion). The MLOps Engineer is responsible for closing the gap between machine learning models development and their operational deployment. This role ensures that machine learning models are efficiently running in the production environment and are continuously monitored for performance. The MLOps Engineer contributes to Arbitration Forums AI-powered portfolio of products and services by enhancing the scalability and reliability of machine learning applications. This role works closely with data scientists, AI engineers, software development, and DevOps teams to automate and streamline the model lifecycle, from development to deployment and monitoring.

Requirements

Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Science, or a related field.
Minimum of 6 years of experience in data science, machine learning, data management, data governance, or a related role.
Minimum of 6 years as a MLOps Engineer or in a similar role.
Working knowledge of cloud services (i.e., MS Azure, AWS, Google Cloud).
Experience with AI tools, such as MS Azure ML, Snowflake, Databricks, CortexAI, Dataiku.
Deep understanding of data science principles, algorithms, and tools.
Strong knowledge of data governance, data security, and compliance practices.
Proficiency in programming languages such as Python, R, or Java.
Experience with containerization tools like Docker and orchestration tools like Kubernetes.
Proficiency in ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
Working knowledge of CI/CD pipelines, DevOps practices, and automation frameworks.
Deep understanding of data engineering concepts and tools.
Familiarity with data visualization and reporting tools (e.g., Webfocus, Power BI).
Excellent analytical and problem-solving abilities.
Strong communication and interpersonal skills to collaborate with cross-functional teams.
Ability to lead projects and mentor junior staff.

Nice To Haves

Auto Insurance claims industry experience preferred.

Responsibilities

Design, implement, and maintain machine learning pipelines and workflows for the continuous deployment and integration of machine learning models. Optimize the pipelines for scalability, efficiency, and cost-effectiveness.
Collaborate with data scientists and AI engineers to understand model requirements and optimize deployment processes.
Automate the training, testing, and deployment processes for machine learning models.
Establish and enforce best practices for version control, documentation, and code quality in ML projects.
Monitor model performance and optimize algorithms for efficiency.
Conduct regular maintenance and updates to deployed models.
Collaborate with cross-functional teams to integrate machine learning solutions into business processes and applications.
Work with go to market, product management, and IT functions as well as stakeholders in AF and its members to identify the optimal methods for model rollout and adoption.
Maintain and optimize the cloud-based machine learning infrastructure and make recommendations for improvements.
Manage and allocate resources effectively, including computer power and storage for model inference.
Develop practices and utilize tools for data validation, model testing, and versioning.
Troubleshoot and resolve machine learning operational issues.
Document processes, workflows, and best practices for ML Operations.
Provide technical leadership and mentorship to junior data team members.
Support data observability efforts to ensure the data continuum and enforce governance standards.
Other duties as assigned by manager or project needs.