Job Information
Robert Half Data Architect: I (Junior) in Columbus, Ohio
Description
Job Title: Data Architect I (Contract-to-Hire)
Location: Hybrid, Columbus, OH
Remote Work: Remote during the 90-day contract period.
Hybrid Expectation: Upon full-time hire, in-office presence is required three days per week.
Contract Duration: 90 days, Contract-to-Hire
Role Summary
As an MLOps Engineer, you will play a pivotal role in the productionization and deployment of machine learning models at scale. You will collaborate with data scientists to optimize ML models for production, enhance the organization's MLOps infrastructure, and automate deployment pipelines. This role also includes developing and managing APIs to enable seamless model integration into the organization’s Kafka-based event-driven architecture. Your contributions will ensure robust, secure, and efficient ML workflows that meet compliance and enterprise IT standards.
Key Responsibilities
Manage and update Docker images to ensure they are secure and high-performing.
Partner with data scientists to validate model compatibility with updated Docker images.
Mitigate security vulnerabilities by patching and upgrading container images.
Deploy, manage, and optimize AWS services such as SageMaker, S3, and Lambda using Terraform.
Automate the provisioning and deprovisioning of cloud infrastructure through infrastructure-as-code principles.
Monitor and enhance AWS resource utilization for cost-effectiveness and performance.
Design, implement, and maintain CI/CD pipelines using Azure DevOps (ADO) for streamlined model deployment workflows.
Collaborate on version control processes using Git for code management and team coordination.
Ensure CI/CD practices align with the deployment lifecycle requirements of ML models.
Partner with stakeholders on the end-to-end deployment of machine learning models into production environments, addressing performance monitoring and retraining needs.
Focus on delivering both near real-time and batch inference solutions, including model drift and data drift monitoring.
Develop, maintain, and document APIs for batch and real-time data integration workflows.
Collaborate with Kafka architects to ensure model output schemas are aligned and functional.
Automate data transfer processes using FTP/SFTP protocols and Kafka APIs as needed for real-time or batch publishing.
Collaborate with a multidisciplinary team that includes data scientists, MLOps peers, and DevOps engineers to foster seamless model integration and innovation.
Stay abreast of emerging MLOps, cloud, and containerization trends and incorporate best practices into the team's workflows.
Requirements
Required Skills & Qualifications
Programming: Advanced expertise in Python for scripting, automation, and API development.
Cloud Expertise: Extensive experience working with AWS services (e.g., SageMaker, S3, Lambda).
Infrastructure-as-Code: Proficiency in using Terraform for cloud resource automation.
Containerization: Mastery of Docker for container image management, deployment, and security.
Operating Systems: Strong proficiency in Linux, especially for command-line operations and Docker execution.
CI/CD Tools: Proven experience utilizing Azure DevOps (ADO) pipelines and workflows.
Version Control: Advanced skills with Git for code versioning and collaboration.
API Development: Experience developing, documenting (e.g., Swagger), and managing APIs with expertise in Kafka-based architectures.
Data Transfer Automation: Knowledge of batch and real-time data transfer mechanisms, including FTP/SFTP, Kafka, and APIs.
Experience: A minimum of 4 years' combined experience in MLOps, DevOps, or Data Engineering roles.
Education: Bachelor’s degree in Computer Science, Engineering, or a related field.
Preferred Qualifications
Familiarity with large language models (LLMs) and their deployment in production environments.
Experience in creating near real-time inference systems and managing batch processing for ML models.
Knowledge of handling challenges related to model drift or data drift in production workloads.
Additional experience with DevOps tools like Jenkins is a plus.
Why Join Us?
This is an exciting opportunity to be hands-on with innovative ML and MLOps technologies in a high-impact role. As a member of our team, you’ll work with cutting-edge practices in a collaborative environment that values innovation, continuous improvement, and technical excellence.
Apply now to collaborate with a forward-thinking team and help shape the future of machine learning in enterprise banking!
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