DevOps / ML Engineer
Kforce Inc.

RESPONSIBILITIES:

Kforce has a client in Phoenix, AZ that is seeking a DevOps/ML Engineer responsible for bridging the gap between machine learning development and operations. They must have a Responsibilities:

• Leads the development and implementation of ML pipelines, including data preprocessing, model training, and deployment workflows
• The Engineer designs and maintains scalable infrastructure for ML model experimentation and testing
• Develops and optimizes automated CI/CD pipelines for the applications, services, models, and prompts
• Implements robust monitoring and logging systems in production, ensuring performance, accuracy, and data drift detection
• Collaborates with data scientists to streamline the process of moving ML models from experimentation to production environments
• Designs and implements strategies for model versioning, A/B testing, and deployments of ML models and prompts
• Develops solutions for efficient data management, including data versioning, feature stores, and data lineage tracking
• Works cross-functionally with data science, software engineering, and operations teams to integrate ML workflows into existing systems and processes
• The Engineer leverages cloud technologies and MLOps tools to enhance the scalability and efficiency of ML operations
• Implements best practices for ML model governance, including model explainability and fairness considerations
• Performs other job-related duties as assigned or apparent

REQUIREMENTS:

• Bachelor's degree in IT or related field
• 5+ years of experience in a MLOps Engineer role and Amazon Web Services including previous experience in creating environments using infrastructure as code for mission critical applications
• Experience with Agile scrum software development methodologies
• Experience in all aspects of the software development lifecycle: design, functional and technical requirements, coding, debugging, testing, release, and operational support
• Significant experience in tools like: Terraform or CloudFormation and Amazon Web Services (Lambda, ApiGateway, IAM, Sagemaker, Bedrock, DynamoDB), MLFlow, Docker, Serverless framework, GitHub, GitHub Actions, Terraform Cloud, bash, Linux, YAML, Python or Node.js
• Strong understanding of machine learning concepts and algorithms, as well as proficiency in programming languages such as Python
• Experience with the AWS cloud platforms along with knowledge of version control systems, CI/CD pipelines, and infrastructure-as-code practices
• Familiar with data processing and storage technologies, including both SQL and NoSQL databases
• Understanding of monitoring and logging tools is crucial for maintaining and optimizing ML models in production
• Experience with model experimentation platforms, model deployment, scaling, and A/B testing
• Strong problem-solving skills and the ability to collaborate effectively with data scientists, software engineers, and other stakeholders are vital for success in this role

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