Lead Machine Learning Ops Engineer

The Friedkin Group - Sugar Land, TX

Full-time - Senior

Sugar Land, TX

Securities, Commodity Contracts, and Other Financial Investments and Related Activities

About the position

As a Lead Machine Learning Ops Engineer, you will play a pivotal role in implementing DevOps and ML Ops practices within the Corporate Data & Analytics Team to support AI/ML application enablement across The Friedkin Group of companies. Your primary responsibility will be to drive the adoption of best practices in DevOps and ML Ops, accelerating the deployment of AI/ML and data-driven solutions that meet our business needs.

Responsibilities

• Develop automated build and deployment processes to enable continuous delivery of software releases.
• Enhance the 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.
• Design, develop and maintain infrastructure using infrastructure as code tools such as Terraform, Ansible, CloudFormation.
• Templatize existing Databricks CLI codes to manage Databricks platform as code for AI/ML data pipelines and model serving endpoints.
• Enhance the 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.
• 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 on code versioning tools, such as Gitlab, GitHub, Azure DevOps, Bitbucket, with GitHub preferred.
• Experience deploying Machine Learning solutions on cloud platforms (e.g., AWS, Azure, or GCP).
• Proficient with GitHub actions to automate testing and deployment of data and ML workloads from CI/CD provider to Databricks.
• Strong knowledge of infrastructure automation tools such as Terraform, Ansible, CloudFormation.
• Experience with data processing frameworks/tools/platform 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.

Benefits

• Comprehensive benefits package including medical, dental, and vision insurance.
• Wellness programs.
• Retirement plans.
• Generous paid leave.