Machine Learning Engineer

Stefanini Group•Dearborn, MI

18h•Onsite

About The Position

Stefanini Group is hiring! Stefanini is looking for a Machine Learning Engineer (Dearborn, MI). We are seeking a Machine Learning Engineer who can build scalable and robust ML data pipelines in the cloud to process large volumes of connected vehicle data to support agentic initiatives.

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.