Machine Learning Engineer

Title: Machine Learning Operations Engineer

Location: Westlake, TX or Smithfield, RI

Duration: 12+ months

W2

Description: This team doesn't actually develop Machine Learning Models, they receive them from the Data Scientists and other ML Engineers in another part of the business. This teams responsibility is to develop REST API's in Python, and take the Models to production, and scale at a production. So, no Data Scientist Candidates or ML Engineers who are looking to only do Model Development, and don't have any API exp.

Interview Process: 2 rounds. Top Skills: REST API Development in Python Machine Learning Operations Experience Experience developing applications in AWS Cloud CI/CD in Jenkins or similar tools.

Machine Learning Ops Engineer
The Role
The FI AI and Analytics Platform team is seeking a passionate Machine Learning Operations Engineer to work on the AI/ML platform in support of our FI Business partners.
As an MLOps Engineer, you will be responsible for developing and maintaining the infrastructure and tools required to support machine learning models deployment and monitoring.
Our team provides modern cloud-based data solutions for the broader FI organization that enables the Sales, Marketing, Finance, and AI/ML Analytics capabilities. This role will be a key contributor of solutions that support the Data Science Agile teams, in the FI Data and Insights Enablement Product Area. The Expertise and Skills You Bring

• Bachelor s or Master s degree in Computer Science, Engineering, or a related field
• 3+ years of experience in MLOps, DevOps, or a related field with a focus on machine learning
• 1+ years of experience developing solutions on the AWS cloud platform
• Proficient in Python and/or Java, with strong coding and debugging skills
• Exposure to continuous integration & delivery (CI/CD) practices
• Proven experience with building container-based systems such as Docker

Responsibilities:

• Model Deployment and Management: Develop and manage the deployment of machine learning models in production environments, ensuring scalability, reliability, and efficiency.
• Infrastructure Automation: Design and implement automated workflows and pipelines for model training, testing, and deployment using tools like Jenkins, Docker, and Kubernetes.
• Aerospike Management: Use Aerospike for high-performance data storage and retrieval, ensuring optimal data handling for machine learning applications.
• API Development: Design, develop, and maintain robust APIs for model integration with various applications and services.
• Collaboration: Work closely with data scientists, software engineers, and other stakeholders to understand requirements and translate them into technical solutions.
• Monitoring and Optimization: Implement monitoring and logging solutions to track model performance and system health, optimizing as necessary to meet business objectives.
• Security and Compliance: Ensure that all deployed models and systems align with industry standards and regulations, implementing vital security measures.