Lead Engineer, MLOps

Summary

Join Code and Theory's Ai/ML engineering team as a Lead ML+DevOps Engineer. You will design and implement MLOps pipelines, manage cloud resources (AWS, GCP, Azure), automate model deployment, and collaborate with data scientists. This high-visibility role requires expertise in cloud deployment, containerization, and related technologies. You will work with internal and external clients to deliver scalable machine learning solutions. The ideal candidate possesses extensive experience in deploying machine learning models to cloud environments and strong expertise in Docker container orchestration. Code and Theory offers a remote-first approach with teams distributed globally.

Requirements

• Extensive experience in deploying machine learning models to cloud environments
• Strong expertise in Docker container orchestration
• Proficiency in Terraform for infrastructure as code (IaC) and cloud resource management
• Hands-on experience with streaming data platforms (e.g., Kafka, Kinesis)
• Solid understanding of data cleaning, transformation, and ETL processes
• Experience with CI/CD tools and pipelines (e.g., Jenkins, GitLab CI)
• Strong programming skills in Python
• Excellent problem-solving skills and the ability to think critically and creatively
• Strong communication skills with the ability to convey technical concepts to non-technical stakeholders

Responsibilities

• Design and implement MLOps pipelines to ensure consistency across the organization
• Configure and manage cloud-based resources (e.g., AWS, GCP, Azure) to support AI/ML workloads, leveraging containerization as needed
• Automate model deployment and management through scripts and tools to streamline the process
• Collaborate with data scientists and engineers to understand their requirements and develop tailored MLOps solutions
• Monitor and optimize AI/ML infrastructure performance by analyzing system performance and identifying bottlenecks
• Stay up-to-date with industry trends and best practices, applying this knowledge to improve our organization's MLOps capabilities

Preferred Qualifications

Familiarity with ML frameworks (e.g., TensorFlow, PyTorch)

Benefits

• Remote-first approach
• The target range of base compensation for this role is $140k-$180k