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
The pay range is the lowest to highest compensation we reasonably in good faith believe we would pay at posting for this role. We may ultimately pay more or less than this range. Employee pay is based on factors like relevant education, qualifications, certifications, experience, skills, seniority, location, performance, union contract and business needs. This range may be modified in the future.
We offer comprehensive benefits including medical/dental/vision insurance, HSA, FSA, 401(k), and life, disability & ADD insurance to eligible employees. Salaried personnel receive paid time off. Hourly employees are not eligible for paid time off unless required by law. Hourly employees on a Service Contract Act project are eligible for paid sick leave.
Note: Pay is not considered compensation until it is earned, vested and determinable. The amount and availability of any compensation remains in Kforce's sole discretion unless
and until paid and may be modified in its discretion consistent with the law.
This job is not eligible for bonuses, incentives or commissions.
Kforce is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, protected veteran status, or disability status.
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