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Machine Learning Senior Engineer

V2Soft, Inc

Dearborn, MI

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JOB DETAILS
JOB TYPE
Contractor
SKILLS
Agile Programming Methodologies, Analysis Skills, Apache Kafka, Application Programming Interface (API), Artificial Intelligence (AI), Atlassian JIRA, Automation, Cloud Architecture, Cloud Computing, Communication Skills, Computer Science, Cost Effectiveness Analysis, Cross-Functional, Customer Support/Service, Customer/Client Research, Data Management, Data Mining, Data Modeling, Data Processing, Data Warehousing, Database Design, DevOps, Docker, Engineering, Expert Systems, GCP (Good Clinical Practices), Git, GitHub, Identify Issues, Information Technology & Information Systems, Information/Data Security (InfoSec), Java, Machine Learning, Mentoring, Microservices, Microsoft SQL Server, Microsoft Windows Azure, MySQL, Open Source, Operations Security (OPSEC), PostgreSQL, Problem Solving Skills, Product Development, Product/Service Launch, Production Costing, Project Management Software, Python Programming/Scripting Language, Quality Metrics, REST (Representational State Transfer), Relational Databases (RDBMS), SQL (Structured Query Language), SQL Databases, Scala Programming Language, Scalable System Development, Software Development, Software Engineering, Software Upgrades, Team Player
LOCATION
Dearborn, MI
POSTED
Today
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 Client 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.

About the Company

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V2Soft, Inc