Machine Learning Engineering Senior Engineer Job Dearborn area,Michigan USA,IT/Tech

Responsibilities

Build scalable and robust ML data pipelines in the cloud to process large volumes of connected vehicle data to support our 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 Dev Ops 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 Sonar Qube, 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
Tensor Flow
Data Governance
Machine Learning
Python
Artificial Intelligence & Expert Systems
Git Hub
Tekton
Docker
Jira
Microservices
Data Architecture
Agile Software Development
SQL
Java
Spark
Cloud Architecture
Apache Kafka
REST APIs

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.
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
Google Cloud Platform
- Deep knowledge of how to implement ML / AI Ops in the GCP Platform specifically is required
Tensor Flow
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.
Machine Learning
- We need an ML Ops expert
Python
- Some of the ML Ops pipeline will likely need to be setup using this code
Artificial Intelligence & Expert Systems
- The ML Ops pipeline needs to be set up for AI Agentic Solutions in mind as well.
Git Hub
- This is where our code will reside, so this is needed SEE 10 TO 21 IN ADDITION INFORMATION

Skills Preferred

Telematics
Machine Learning
Data Modeling
Cloud Infrastructure
Data Mining
Database Design
Troubleshooting (Problem Solving)
Labor Supervision

Telematics
- Knowledge of this is nice, as some of our data will be Telematics data
Machine Learning
Data Modeling
- In order to understand how the data will interact with the ML Operations.
Cloud Infrastructure
Data Mining
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