Mgr-ML Enablement Engineering

ML Enablement Engineering Manager

Position Overview

As the Machine Learning Enablement Engineering Manager within Fords Data Platforms and Engineering (DP&E) organization you are a key leader responsible for guiding and developing a team of engineers focused on delivering highimpact scalable machine learning solutions to address critical business challenges within DP&E. Your primary focus will be on building and maintaining the platform infrastructure and processes that empower data scientists and ML engineers to rapidly deploy and scale their solutions into production. You will work closely with Product Managers Architects Data Scientists and other key stakeholders to drive engineering excellence promote innovation and uphold best practices. This role is less about building individual ML models and more about creating robust reliable and scalable solutions that allow others to deliver value effectively.

Your leadership is crucial in driving the success of our machine learning initiatives. Your ability to guide and develop a team of engineers while maintaining alignment with Fords strategic goals will be key to delivering worldclass productionready ML solutions that power Fords transformation into a datadriven enterprise.

You should be a highly handson engineering leader with a proven track record of delivering complex scalable solutions. While a deep understanding of ML concepts is beneficial your primary focus will be on platform engineering DevOps and building robust maintainable infrastructure. You will define processes for technical platforms conceive application prototypes and mentor your team in best practices. Your daytoday responsibilities will involve designing and managing the organizations ML infrastructure architecture ensuring data is efficiently processed stored and accessed to support ML model development and deployment. You will be pivotal in delivering these solutions on time and within budget.

I. Engineering Leadership & Management:

• Proven experience 7 years) in a leadership role managing engineering teams ideally with a focus on platform engineering MLOps or similar areas. Experience managing remote teams is a plus.
• Experience leading and mentoring engineering teams fostering a culture of innovation continuous learning and technical excellence. Demonstrated ability to drive strategic technical decisions and ensure alignment with broader organizational goals.
• Proven ability to build and maintain highperforming teams promoting accountability ownership and collaboration. Experience with performance management including conducting performance reviews and providing constructive feedback.
• Excellent communication and interpersonal skills with a proven ability to cultivate crossfunctional collaboration and build strong relationships with stakeholders at all levels.

II. Agile & Scrum Practices:

• Deep understanding and practical experience with Agile methodologies (Scrum Kanban) including facilitating daily standups sprint planning backlog grooming and sprint retrospectives.
• Experience working closely with Product Managersto align engineering efforts with product goals ensure welldefined user stories and manage priorities effectively.
• Proven ability to ensure engineering rigor in story hygiene including clear acceptance criteria welldefined dependencies and a focus on deliverability within the sprint.

III. Technical Expertise & Accountability:

• Deep understanding of platform engineering principlesand experience designing building and maintaining scalable and reliable infrastructure for ML workloads.
• Expertise in DevOps practices including CI/CD pipelines (Jenkins GitLab CI GitHub Actions) infrastructureascode (Terraform Ansible CloudFormation) and automation.
• Proficiency in at least one programming language(e.g. Python Java) sufficient to effectively communicate with and guide your engineering team. You wont be expected to contribute to team capacity by coding but you need to be able to speak the language of your engineers.
• Strong understanding of cloud solutions and offerings(preferably GCP services Compute Engine Kubernetes Engine Cloud Functions BigQuery Pub/Sub Cloud Storage Vertex AI). Experience with other major cloud providers (AWS Azure) is also valuable.
• Experience with designing and implementing microservices and serverless architectures.Experience with containerization (Docker Kubernetes) is highly beneficial.
• Experience with monitoring and optimizing platform performance ensuring systems are running efficiently and meeting SLAs. Proven ability to lead incident management efforts and implement continuous improvements to enhance reliability.
• Commitment to best engineering practices including code reviews testing and documentation. A focus on building maintainable and scalable systems is essential.

IV. Operational Excellence & Cost Optimization:

• Proven ability to drive cost optimization initiatives particularly in cloud infrastructure and resource usage aligning with Fords broader costreduction goals.
• Experience tracking and reporting key metricsfor your domain/platform related to team performance including quality and operational efficiency.

I. Engineering Leadership & Management:

• Proven experience 7 years) in a leadership role managing engineering teams ideally with a focus on platform engineering MLOps or similar areas. Experience managing remote teams is a plus.
• Experience leading and mentoring engineering teams fostering a culture of innovation continuous learning and technical excellence. Demonstrated ability to drive strategic technical decisions and ensure alignment with broader organizational goals.
• Proven ability to build and maintain highperforming teams promoting accountability ownership and collaboration. Experience with performance management including conducting performance reviews and providing constructive feedback.
• Excellent communication and interpersonal skills with a proven ability to cultivate crossfunctional collaboration and build strong relationships with stakeholders at all levels.

II. Agile & Scrum Practices:

• Deep understanding and practical experience with Agile methodologies (Scrum Kanban) including facilitating daily standups sprint planning backlog grooming and sprint retrospectives.
• Experience working closely with Product Managersto align engineering efforts with product goals ensure welldefined user stories and manage priorities effectively.
• Proven ability to ensure engineering rigor in story hygiene including clear acceptance criteria welldefined dependencies and a focus on deliverability within the sprint.

III. Technical Expertise & Accountability:

• Deep understanding of platform engineering principlesand experience designing building and maintaining scalable and reliable infrastructure for ML workloads.
• Expertise in DevOps practices including CI/CD pipelines (Jenkins GitLab CI GitHub Actions) infrastructureascode (Terraform Ansible CloudFormation) and automation.
• Proficiency in at least one programming language(e.g. Python Java) sufficient to effectively communicate with and guide your engineering team. You wont be expected to contribute to team capacity by coding but you need to be able to speak the language of your engineers.
• Strong understanding of cloud solutions and offerings(preferably GCP services Compute Engine Kubernetes Engine Cloud Functions BigQuery Pub/Sub Cloud Storage Vertex AI). Experience with other major cloud providers (AWS Azure) is also valuable.
• Experience with designing and implementing microservices and serverless architectures.Experience with containerization (Docker Kubernetes) is highly beneficial.
• Experience with monitoring and optimizing platform performance ensuring systems are running efficiently and meeting SLAs. Proven ability to lead incident management efforts and implement continuous improvements to enhance reliability.
• Commitment to best engineering practices including code reviews testing and documentation. A focus on building maintainable and scalable systems is essential.

IV. Operational Excellence & Cost Optimization:

• Proven ability to drive cost optimization initiatives particularly in cloud infrastructure and resource usage aligning with Fords broader costreduction goals.
• Experience tracking and reporting key metricsfor your domain/platform related to team performance including quality and operational efficiency.