Position: Machine Learning Engineering Senior Engineer- 327910
Position Description:
ML Ops Build scalable and robust ML data pipelines in the cloud to process large volumes of connected vehicle data to support Ford's agentic initiatives. Optimize existing ML solutions for performance, security, and cost-effectiveness Utilize continual learning methods to continuously improve model performance Other Develop exceptional analytical data products using both streaming and batch ingestion patterns on Google Cloud Platform with solid data warehouse principles. Build data pipelines to monitoring quality of data and performance of analytical models and agentic solutions. Maintain the infrastructure of the data platform using terraform and continuously develop, evaluate, and deliver code using CI/CD. Collaborate with data analytics stakeholders to streamline the data acquisition, processing, and presentation process. Implement an enterprise data governance model and actively promote the concept of data - protection, sharing, reuse, quality, and standards. Enhance and maintain the DevOps capabilities of the data platform. Continuously optimize and enhance existing data solutions (pipelines, products, infrastructure) for best performance, high security, low vulnerability, low costs, and high reliability. Work in an agile product team to deliver code frequently using Test Driven Development (TDD), continuous integration and continuous deployment (CI/CD). Promptly address code quality issues using SonarQube, Checkmarx, Fossa, and Cycode throughout the development lifecycle. Perform any necessary data mapping, data lineage activities and document information flows. Monitor the production pipelines and provide production support by addressing production issues as per SLAs. Provide analysis of connected vehicle data to support new product developments and production vehicle improvements. Provide visibility to data quality/vehicle/feature issues and work with the business owners to fix the issues. Demonstrate technical knowledge and communication skills with the ability to advocate for well-designed solutions. Continuously enhance your domain knowledge of connected vehicle data, connected services and algorithms/models/solutions developed by data scientists and AI engineers. Stay current on the latest data engineering practices and contribute to the technical direction of the company while keeping a customer-centric approach.
Skills Required:
Technical Communication, Communications, Google Cloud Platform, TensorFlow, Data Governance, Machine Learning, Python, Artificial Intelligence & Expert Systems, GitHub, Tekton, Docker, Jira, Microservices, Data Architecture, Agile Software Development, SQL, Java, Spark, Cloud Architecture, Apache Kafka, REST APIs
1. Technical Communication This person will need to describe clearly the ML/AI Ops needs and strategy to colleagues potentially up to executives across a wide cross section of people from very knowledge to not technically knowledgeable in this area.
2. Communications In addition to the technical communication needed, this person will need to be a great communicator to work with people in other organizations who are stakeholders and we need to work together and not have there be communication gaps
3. Google Cloud Platform Deep knowledge of how to implement ML / AI Ops in the Google Cloud Platform Platform specifically is required
4. TensorFlow
5. Data Governance This role will need to implement an enterprise data governance model and actively promote the concept of data - protection, sharing, reuse, quality, and standards.
6. Machine Learning We need an ML Ops expert
7. Python Some of the ML Ops pipeline will likely need to be setup using this code
8. Artificial Intelligence & Expert Systems The ML Ops pipeline needs to be set up for AI Agentic Solutions in mind as well.
9. GitHub This is where our code will reside, so this is needed SEE 10 TO 21 IN ADDITION INFORMATION
