Lead Machine Learning Operations Engineer

3 weeks ago

Houston, Texas, United States The Friedkin Group Full time
About the Role

We are seeking a highly skilled Lead Machine Learning Operations Engineer to join our Corporate Data & Analytics Team at The Friedkin Group. In this role, you will be responsible for implementing DevOps and ML Ops practices to support AI/ML application enablement across our companies.

Key Responsibilities
  • Develop automated build and deployment processes to enable continuous delivery of software releases, enhancing existing CI/CD pipelines for AI/ML application development and deployment.
  • Collaborate with data scientists, data engineers, data analysts, software engineers, IT specialists, and stakeholders to accelerate deployment of AI applications via CI/CD pipelines and maintain the SLAs of those applications at the centralized platform.
  • Design, develop, and maintain infrastructure using infrastructure as code tools such as Terraform, Ansible, CloudFormation, etc.
  • Templatize existing Databricks CLI codes to manage Databricks platform as code for AI/ML data pipelines (batch processing, batch streaming, and streaming) and model serving endpoints.
  • Enhance existing DevOps practices to improve the overall AI/ML application development lifecycle.
  • Work closely with cross-functional teams to ensure that applications are highly available and scalable.
  • Collaborate with development teams and cloud platform teams to ensure that infrastructure meets the requirements of the application.
  • Establish and maintain best practices for cloud security, compliance, and cost optimization.
Requirements
  • Bachelor's Degree in Computer Science, Computer Engineering, Information Technology, Software Engineering, or equivalent technical discipline, and 10+ years of experience in software engineering with a strong background in DevOps and Infrastructure as Code, supporting Machine Learning and Data Science workloads preferred.
  • Master's Degree in Computer Science, Computer Engineering, Information Technology, Software Engineering, or equivalent technical discipline, and 5+ years of experience in software engineering with a strong background in DevOps and Infrastructure as Code, supporting Machine Learning and Data Science workloads preferred.
  • Expertise in code versioning tools, such as GitLab, GitHub, Azure DevOps, Bitbucket, etc., GitHub preferred, familiar with branch-level code repository management.
  • Experience deploying Machine Learning solutions on cloud platforms (e.g., AWS, Azure, or GCP). Databricks and AWS preferred.
  • Proficient with GitHub Actions to automate testing and deployment of data and ML workloads from CI/CD providers to Databricks.
  • Strong knowledge of infrastructure automation tools such as Terraform, Ansible, CloudFormation, etc.
  • Experience with data processing frameworks/tools/platforms such as Databricks, Apache Spark, Kafka, Flink, AWS cloud services for batch processing, batch streaming, and streaming.
  • Experience containerizing analytical models using Docker and Kubernetes or other container orchestration platforms.
  • Technical expertise across all deployment models on public cloud, private cloud, and on-premises infrastructure.
  • Experience in event-driven and microservice architectures for enterprise-level platform development.
  • Expertise in Linux, and knowledge of networking and security concepts.
  • Effective communication skills and a sense of ownership and drive.
  • Capable of coaching/mentoring individuals and teams.

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