About
(Machine Learning Engineering Senior Engineer
About Kyyba:
Founded in 1998 and headquartered in Farmington Hills, MI, Kyyba has a global presence delivering high-quality resources and top-notch recruiting services, enabling businesses to effectively respond to organizational changes and technological advances.
At Kyyba, the overall well-being of our employees and their families is important to us. We are proud of our work culture which embodies our core values; incorporating value, passion, excellence, empowerment, and happiness, creates a vibrant and productive atmosphere. We empower our employees with the resources, incentives, and flexibility that they need to support a healthy, balanced, and fulfilling career by providing many valuable benefits and a balanced compensation structure combined with career development.
Job Description
ML Ops • Build scalable and robust ML data pipelines in the cloud to process large volumes of connected vehicle data to support ***'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 GCP 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
Skills Preferred:
Telematics, Machine Learning, Data Modeling, Cloud Infrastructure, Data Mining, Database Design, Troubleshooting (Problem Solving), Labor Supervision 1. Telematics – Knowledge of this is nice, as some of our data will be Telematics data 2. Machine Learning – 3. Data Modeling – In order to understand how the data will interact with the ML Operations. 4. Cloud Infrastructure – 5. Data Mining – 6. Database Design – 7. Troubleshooting (Problem Solving) – 8. Labor Supervision – Will need to mentor and advise junior team members to spread ML Ops expertise across the organization
Experience Required:
• 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: o Data engineering, data product development and software product launches o 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: o Data warehouses like Amazon Redshift, Microsoft Azure Synapse Analytics, Google BigQuery. o Workflow orchestration tools like Airflow. o Relational Database Management System like MySQL, PostgreSQL, and SQL Server. o Real-Time data streaming platform like Apache Kafka, GCP Pub/Sub o Microservices architecture to deliver large-scale real-time data processing application. o REST APIs for compute, storage, operations, and security. o DevOps tools such as Tekton, GitHub Actions, Git, GitHub, Terraform, Docker. o Project management tools like Atlassian JIRA. Even better if you have...
Experience Preferred:
• 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
Education Required:
Bachelor's Degree
Education Preferred:
Master's Degree
Additional Information:
***HYBRID / 4 days per week in the office*** 10. Tekton – Will likely be needed to work in our DevOps 11. Docker – Our vendor will be using Docker images, so we will need to know how to account for this. 12. Jira – Our projects are managed in Jira, so knowledge of Jira would be nice. 13. Microservices – Microservices architecture to deliver large-scale real-time data processing application. 14. Data Architecture – Optimize existing ML solutions for performance, security, and cost-effectiveness 15. Agile Software Development – Need to be able to work in an Agile environment, related to Jira and Communication skills 16. SQL – There will be SQL in the pipeline, so knowledge is important 17. Java – May be in the pipeline 18. Spark – May be in the pipeline 19. Cloud Architecture – Knowledge to Build scalable and robust ML data pipelines in the cloud to process large volumes of connected vehicle data to support ***'s agentic initiatives. 20. Apache Kafka – Knowledge of this for real time data streaming in the pipeline is important 21. REST APIs – REST APIs for compute, storage, operations, and security.
Location
: (
Dearborn, MI
)
Disclaimer:
Kyyba is an Equal Opportunity Employer.
Kyyba does not discriminate on the basis of race, religion, color, sex, gender identity, sexual orientation, age, non-disqualifying physical or mental disability, national origin, veteran status or any other basis covered by appropriate law. Minorities / Females / Protected Veterans / Individuals with Disabilities are encouraged to apply. All employment is decided on the basis of qualifications, merit, and business need.”
It is the policy of Kyyba to provide reasonable accommodation when requested by a qualified applicant or employee with a disability, unless such accommodation would cause an undue hardship. The policy regarding requests for reasonable accommodation applies to all aspects of employment, including the application process. If reasonable accommodation is needed, please contact Kyyba at 248-813-9665
Rewards:
Medical, dental, vision
401k
Term life
Voluntary life and disability insurance
Optional Pre-paid legal plan
Optional Identity theft plan
Optional Medical and dependent FSA
Work-visa sponsorship
Opportunity for advancement
Long-term assignment with opportunity for hire by client
SELECT AWARDS
An INC 5000 company for 10 years
Corp! Michigan Economic Bright Spots
Crain’s Detroit Business Top Staffing Service Companies in Detroit
TechServe Alliance Excellence Award- IT and Engineering Staffing & Solutions
Best of MichBusiness winner in HR Wizards & Partnerships
Metro Detroit Elite Category: Recruitment, Selection & Orientation for 101 Best & Brightest
101 Best & Brightest Companies to Work for in Michigan
Languages
- English
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