Machine Learning Engineer

Stefanini Group•Dearborn, MI

2d•Onsite

About The Position

Stefanini Group is hiring a Machine Learning Engineer in Dearborn, MI. This role involves building scalable and robust ML data pipelines in the cloud to process large volumes of connected vehicle data to support agentic initiatives. The engineer will optimize existing ML solutions for performance, security, and cost-effectiveness, and develop analytical data products using both streaming and batch ingestion patterns on Google Cloud Platform with solid data warehouse principles. Responsibilities include building data pipelines for monitoring data quality and model performance, maintaining data platform infrastructure using terraform, and continuously developing code using CI/CD. The role also involves collaborating with data analytics stakeholders, implementing an enterprise data governance model, enhancing DevOps capabilities, and working in an agile product team using Test Driven Development (TDD), continuous integration, and continuous deployment (CI/CD). Promptly addressing code quality issues using SonarQube, Checkmarx, Fossa, and Cycode, performing data mapping and lineage activities, monitoring production pipelines, providing production support, and analyzing connected vehicle data for product development are also key aspects of this position. Continuous enhancement of domain knowledge in connected vehicle data, services, and algorithms is expected.

Requirements

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
Master's degree or foreign equivalent degree in Computer Science, Software Engineering, Information Systems, Data Engineering, or a related field, and 4 years of experience OR equivalent combination of education and experience (6+ years with Bachelor's Degree)
4 years of professional experience in: Data engineering, data product development and software product launches
At least three of the following languages: Java, Python, Spark, Scala, SQL
3 years of cloud data/software engineering experience building scalable, reliable, and cost-effective production batch and streaming data pipelines using: Data warehouses like Amazon Redshift, Microsoft Azure Synapse Analytics, Google BigQuery
Workflow orchestration tools like Airflow
Relational Database Management System like MySQL, PostgreSQL, and SQL Server
Real-Time data streaming platform like Apache Kafka, GCP Pub/Sub
Microservices architecture to deliver large-scale real-time data processing application
REST APIs for compute, storage, operations, and security
DevOps tools such as Tekton, GitHub Actions, Git, GitHub, Terraform, Docker
Project management tools like Atlassian JIRA

Nice To Haves

Telematics
Machine Learning
Data Modeling
Cloud Infrastructure
Data Mining
Database Design
Troubleshooting (Problem Solving)
Labor Supervision
Ph.D. or foreign equivalent degree in Computer Science, Software Engineering, Information System, Data Engineering, or a related field
2 years of experience with ML Model Development and/or MLOps
Committed code to improve open-source data/software engineering projects
Experience architecting cloud infrastructure and handling application migrations/upgrades
GCP Professional Certifications
Demonstrated passion to mine raw data and realize its hidden value
Passion to experiment/implement state of the art data engineering methods/techniques
Experience working in an implementation team from concept to operations, providing deep technical subject matter expertise for successful deployment
Experience implementing methods for automation of all parts of the pipeline to minimize labor in development and production
Analytics skills to profile data, troubleshoot data pipeline/product issues
Ability to simplify, clearly communicate complex data/software ideas/problems and work with cross-functional teams and all levels of management independently
Ability to mentor and advise junior team members

Responsibilities

Optimize existing ML solutions for performance, security, and cost-effectiveness
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
Continuously enhance your domain knowledge of connected vehicle data, connected services and algorithms/models/solutions developed by data scientists and AI engineers