ShiftCode Analytics, Inc. · 3 hours ago

MLOps Engineer

United States

Contract

Remote

Mid, Senior Level

5+ years exp

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Responsibilities

Leads the development and implementation of ML pipelines, including data preprocessing, model training, and deployment workflows.

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.

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.

Qualification

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Software Development LifecycleAmazon Web ServicesTerraformPythonMachine Learning ConceptsDockerGitHubCI/CD PipelinesYAMLNode.jsLinuxVersion Control SystemsSQL DatabasesNoSQL DatabasesMonitoring ToolsA/B TestingAgile Scrum

Required

Experience in all aspects of the software development lifecycle: design, functional and technical requirements, coding, debugging, testing, release, and operational support.

Significant experience using and leading others in tools like: Terraform or CloudFormation and Amazon Web Services (Lambda, API Gateway, 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, the AWS cloud platforms, and 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.

Experience with agile scrum software development methodologies.

Bachelor's degree in IT or related field.

Minimum of 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.