The Friedkin Group
Lead Machine Learning Ops Engineer
The Friedkin Group, Houston, Texas, us, 77240
Living Our ValuesAll associates are guided by Our Values. Our Values are the unifying foundation of our companies. We strive to ensure that every decision we make and every action we take demonstrates Our Values. We believe that putting Our Values into practice creates lasting benefits for all of our associates, shareholders, and the communities in which we live.
Why Join Us
Career Growth: Advance your career with opportunities for leadership and personal development.Culture of Excellence: Be part of a supportive team that values your input and encourages innovation.Competitive Benefits: Enjoy a comprehensive benefits package that looks after both your professional and personal needs.Total RewardsOur Total Rewards package underscores our commitment to recognizing your contributions. We offer a competitive and fair compensation structure that includes base pay and performance-based rewards. Compensation is based on skill set, experience, qualifications, and job-related requirements. Our comprehensive benefits package includes medical, dental, and vision insurance, wellness programs, retirement plans, and generous paid leave. Discover more about what we offer by visiting our
Benefits
page.
A Day In The LifeAs 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. We seek a motivated and skilled individual with a strong background in DevOps and ML Ops, a deep understanding of Infra Ops, and solid knowledge of AI/ML data and analytics cloud services and components. You will collaborate closely with data scientists, machine learning engineers, data engineers, software engineers, and platform architects, utilizing the latest tools and technologies to deploy and maintain AI/ML and advanced analytics solutions, as well as integrate analytic models with existing business applications.
As a Lead Machine Learning Ops Engineer you will:Develop automated build and deployment processes to enable continuous delivery of software releases, enhance the existing CI/CD pipelines for AIML 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 AIML data pipelines (batch processing, batch streaming and streaming) and model serving endpoints.Enhance the existing DevOps practices to improve the overall AIML 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 team to ensure that infrastructure meets the requirements of the application.Establish and maintain best practices for cloud security, compliance, and cost optimization.
What We Need From You
Bachelor's Degree 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. orMaster's Degree 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 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 provider to Databricks.Strong knowledge of infrastructure automation tools such as Terraform, Ansible, CloudFormation etc.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 conceptsEffective communication skills and a sense of ownership and drive.Capable of coaching/mentoring individuals and teams.Physical and Environmental Requirements
The physical requirements described here are representative of those that must be met by an associate to successfully perform the essential functions of the job. While performing the duties of the job, the associate is required on a daily basis to analyze and interpret data, communicate, and remain in a stationary position for a significant amount of the work day and frequently access, input, and retrieve information from the computer and other office productivity devices. The associate is regularly required to move about the office and around the corporate campus. The associate must frequently move up to 10 pounds and occasionally move up to 25 pounds.
Travel Requirements20% The associate is occasionally required to travel to other sites, including out-of-state, where applicable, for business.
Join UsThe Friedkin Group and its affiliates are committed to ensuring equal employment opportunities, including providing reasonable accommodations to individuals with disabilities. If you have a disability and would like to request an accommodation, please contact us at TalentAcquisition@friedkin.com. We celebrate diversity and are committed to creating an inclusive environment for all associates.
We are seeking candidates legally authorized to work in the United States, without Sponsorship.
#LI-BM1
#TN125
Lead Machine Learning Ops Engineer at The Friedkin Group summary:The Lead Machine Learning Ops Engineer will implement DevOps and Machine Learning Operations practices to enhance AI/ML application deployment within the Corporate Data & Analytics Team. This role requires expertise in building automated CI/CD pipelines and infrastructure as code, working collaboratively with cross-functional teams to ensure the scalability and security of applications. Candidates should have a strong background in software engineering, cloud platforms, and machine learning workloads, with a focus on driving best practices in a dynamic environment.
Keywords:Machine Learning Ops, DevOps, AI applications, CI/CD pipelines, Infrastructure as Code, Cloud Platforms, Data Processing, Automation, Software Engineering, Data Analytics
Why Join Us
Career Growth: Advance your career with opportunities for leadership and personal development.Culture of Excellence: Be part of a supportive team that values your input and encourages innovation.Competitive Benefits: Enjoy a comprehensive benefits package that looks after both your professional and personal needs.Total RewardsOur Total Rewards package underscores our commitment to recognizing your contributions. We offer a competitive and fair compensation structure that includes base pay and performance-based rewards. Compensation is based on skill set, experience, qualifications, and job-related requirements. Our comprehensive benefits package includes medical, dental, and vision insurance, wellness programs, retirement plans, and generous paid leave. Discover more about what we offer by visiting our
Benefits
page.
A Day In The LifeAs 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. We seek a motivated and skilled individual with a strong background in DevOps and ML Ops, a deep understanding of Infra Ops, and solid knowledge of AI/ML data and analytics cloud services and components. You will collaborate closely with data scientists, machine learning engineers, data engineers, software engineers, and platform architects, utilizing the latest tools and technologies to deploy and maintain AI/ML and advanced analytics solutions, as well as integrate analytic models with existing business applications.
As a Lead Machine Learning Ops Engineer you will:Develop automated build and deployment processes to enable continuous delivery of software releases, enhance the existing CI/CD pipelines for AIML 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 AIML data pipelines (batch processing, batch streaming and streaming) and model serving endpoints.Enhance the existing DevOps practices to improve the overall AIML 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 team to ensure that infrastructure meets the requirements of the application.Establish and maintain best practices for cloud security, compliance, and cost optimization.
What We Need From You
Bachelor's Degree 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. orMaster's Degree 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 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 provider to Databricks.Strong knowledge of infrastructure automation tools such as Terraform, Ansible, CloudFormation etc.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 conceptsEffective communication skills and a sense of ownership and drive.Capable of coaching/mentoring individuals and teams.Physical and Environmental Requirements
The physical requirements described here are representative of those that must be met by an associate to successfully perform the essential functions of the job. While performing the duties of the job, the associate is required on a daily basis to analyze and interpret data, communicate, and remain in a stationary position for a significant amount of the work day and frequently access, input, and retrieve information from the computer and other office productivity devices. The associate is regularly required to move about the office and around the corporate campus. The associate must frequently move up to 10 pounds and occasionally move up to 25 pounds.
Travel Requirements20% The associate is occasionally required to travel to other sites, including out-of-state, where applicable, for business.
Join UsThe Friedkin Group and its affiliates are committed to ensuring equal employment opportunities, including providing reasonable accommodations to individuals with disabilities. If you have a disability and would like to request an accommodation, please contact us at TalentAcquisition@friedkin.com. We celebrate diversity and are committed to creating an inclusive environment for all associates.
We are seeking candidates legally authorized to work in the United States, without Sponsorship.
#LI-BM1
#TN125
Lead Machine Learning Ops Engineer at The Friedkin Group summary:The Lead Machine Learning Ops Engineer will implement DevOps and Machine Learning Operations practices to enhance AI/ML application deployment within the Corporate Data & Analytics Team. This role requires expertise in building automated CI/CD pipelines and infrastructure as code, working collaboratively with cross-functional teams to ensure the scalability and security of applications. Candidates should have a strong background in software engineering, cloud platforms, and machine learning workloads, with a focus on driving best practices in a dynamic environment.
Keywords:Machine Learning Ops, DevOps, AI applications, CI/CD pipelines, Infrastructure as Code, Cloud Platforms, Data Processing, Automation, Software Engineering, Data Analytics