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# Senior Machine Learning Engineer

- **Company:** Megan Soft, Inc.
- **Location:** Dearborn, MI, United States
- **Experience:** Expert
- **Salary:** $124,800.0 - $145,600.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Agile Methodology, Artificial Intelligence, Airflow, JIRA, Cloud Computing, Cloud Engineering, Software Quality, Information Systems, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Data Mining, Data Profiling, Database Design, DevOps, Github, Python (Programming Language), PostgreSQL, Machine Learning, Microsoft SQL Server, MySQL, Open Source Technology, Operational Databases, Tensorflow, Standard Sql, Software Engineering, SonarQube, Data Streaming, Data Processing, Google Cloud, Test-Driven Development (TDD), Apache Spark, Information Technology, Data Lineage, Data Analytics, Google Bigquery, Apache Kafka, Machine Learning Operations, Checkmarx, Restful APIs, Terraform, Azure Synapse Analytics, Data Pipelines, Docker, Microservices
- **Published:** July 22, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=74f99ec032c2a050

## About the Role

* Google Cloud Platform (GCP)
 * Machine Learning / MLOps
 * TensorFlow
 * Python
 * Java
 * Spark
 * SQL
 * Artificial Intelligence / AI
 * Data Governance
 * Data Architecture
 * Cloud Architecture
 * Apache Kafka
 * REST APIs
 * Microservices
 * Git / GitHub / GitHub Actions
 * Terraform
 * Tekton
 * Docker
 * Jira
 * Agile Software Development
 * Strong Technical Communication & Collaboration Skills

Preferred Skills

 * Telematics
 * Data Modeling
 * Cloud Infrastructure
 * Data Mining
 * Database Design
 * Troubleshooting & Problem Solving
 * Leadership / Mentoring Experience, * Master's degree with 4+ years of experience, or Bachelor's degree with 6+ years of relevant experience.
 * 4+ years of Data Engineering and software product development experience.
 * Strong experience with at least three of the following:
 * Python
 * Java
 * Spark
 * Scala
 * SQL
 * 3+ years building cloud-based production data pipelines using:
 * Google BigQuery, Redshift, or Azure Synapse
 * Airflow
 * MySQL, PostgreSQL, or SQL Server
 * Apache Kafka or GCP Pub/Sub
 * Microservices
 * REST APIs
 * Terraform
 * Docker
 * GitHub Actions
 * Tekton
 * Atlassian Jira

Preferred Experience

 * Ph.D. in Computer Science, Software Engineering, Information Systems, or related field.
 * 2+ years of ML Model Development and/or MLOps experience.
 * Experience with cloud architecture and application migrations.
 * GCP Professional Certifications.
 * Experience contributing to open-source projects.
 * Strong analytics and data profiling skills.
 * Experience implementing end-to-end automation across ML pipelines.
 * Excellent communication and stakeholder management skills.
 * Experience mentoring junior engineers.

EducationRequired: Bachelor's Degree in Computer Science, Software Engineering, Information Systems, Data Engineering, or related field.Preferred: Master's Degree or Ph.D.Work Schedule

## Description

Duration: 12 MonthsPosition OverviewWe are seeking an experienced ML Ops Engineer to design, build, and optimize scalable machine learning data pipelines on Google Cloud Platform (GCP). The ideal candidate will have strong expertise in MLOps, Data Engineering, DevOps, Cloud Infrastructure, and Machine Learning to support Ford's connected vehicle and AI/Agentic initiatives.The role involves developing robust batch and streaming data pipelines, implementing enterprise data governance, maintaining cloud infrastructure, optimizing ML solutions, and collaborating with cross-functional teams to deliver high-quality data products.Key Responsibilities

 * Design, develop, and maintain scalable ML data pipelines on Google Cloud Platform.
 * Build batch and streaming data pipelines for connected vehicle data.
 * Optimize ML solutions for performance, scalability, security, reliability, and cost.
 * Develop and maintain cloud infrastructure using Terraform and CI/CD pipelines.
 * Build and monitor production data pipelines while providing production support.
 * Implement enterprise data governance, data lineage, and data quality standards.
 * Collaborate with data scientists, AI engineers, and business stakeholders.
 * Enhance DevOps capabilities using GitHub, Tekton, Docker, and GitHub Actions.
 * Deliver software using Agile methodologies, Test-Driven Development (TDD), CI/CD, and DevOps best practices.
 * Resolve code quality issues using SonarQube, Checkmarx, FOSSA, and Cycode.
 * Design Microservices and REST APIs for scalable data processing.
 * Support AI Agentic initiatives and connected vehicle analytics.
 * Troubleshoot production issues and ensure SLA compliance.
 * Continuously improve data engineering solutions and cloud infrastructure.


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