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Data and AI Engineer - Automotive Engineering Analytics
Artech
4 hours ago
Full-time
On-site
Warren, Michigan, United States
Job ID: 108766-1
Title: Data and AI Engineer - Automotive Engineering Analytics
Location: Warren, OH (ONSITE)
Duration: 12+ Months
Pay Range: $45 - $48 an hour on W2/ C2C (All Inclusive)
Role: Data and AI Engineer - Automotive Engineering Analytics Must Have Skills: Must Have: Strong experience in Python, SQL, Data Engineering, and Analytics. Experience with Automotive, Manufacturing, Engineering, Quality, Reliability, Warranty, or Telemetry data. Hands-on experience with Machine Learning, Statistical Analysis, and AI/GenAI technologies (LLMs, RAG, Embeddings, Vector Search, Knowledge Graphs). Experience building ETL/Data Pipelines on Cloud platforms. Experience with dashboards, web applications, APIs, and data visualization. Knowledge of CI/CD, testing frameworks, and data quality monitoring. Ability to process structured and unstructured data and collaborate with business stakeholders. Responsibilities:
Build and maintain scalable data pipelines and analytical solutions. Clean, transform, validate, and analyze data from multiple sources. Develop ML models and AI-powered applications. Implement LLM/RAG-based solutions and ensure accuracy, traceability, and data security. Create dashboards, reports, and actionable insights for engineering teams. Monitor model performance, data quality, and system reliability. Pre-Screening Questionnaire Describe your experience using Python and SQL to clean, transform, join, validate, and analyze data from multiple sources.
Describe one machine-learning or statistical-analysis project you delivered. What methods, evaluation metrics, validation approach, and business or engineering outcome were involved?
What hands-on experience do you have with AI-enabled applications such as LLMs, retrieval-augmented generation, embeddings, vector search, or knowledge graphs, and how did you address accuracy, traceability, human review, and data protection?
Describe a production-grade data pipeline you designed or supported. How did you handle ingestion, transformation, missing or duplicate records, validation, data lineage, and scalability? • How would you monitor a deployed machine-learning model for drift, changing data quality, false positives, false negatives, and performance regression?
Role: Data and AI Engineer - Automotive Engineering Analytics Must Have Skills: Must Have: Strong experience in Python, SQL, Data Engineering, and Analytics. Experience with Automotive, Manufacturing, Engineering, Quality, Reliability, Warranty, or Telemetry data. Hands-on experience with Machine Learning, Statistical Analysis, and AI/GenAI technologies (LLMs, RAG, Embeddings, Vector Search, Knowledge Graphs). Experience building ETL/Data Pipelines on Cloud platforms. Experience with dashboards, web applications, APIs, and data visualization. Knowledge of CI/CD, testing frameworks, and data quality monitoring. Ability to process structured and unstructured data and collaborate with business stakeholders. Responsibilities:
Build and maintain scalable data pipelines and analytical solutions. Clean, transform, validate, and analyze data from multiple sources. Develop ML models and AI-powered applications. Implement LLM/RAG-based solutions and ensure accuracy, traceability, and data security. Create dashboards, reports, and actionable insights for engineering teams. Monitor model performance, data quality, and system reliability. Pre-Screening Questionnaire Describe your experience using Python and SQL to clean, transform, join, validate, and analyze data from multiple sources.
Describe one machine-learning or statistical-analysis project you delivered. What methods, evaluation metrics, validation approach, and business or engineering outcome were involved?
What hands-on experience do you have with AI-enabled applications such as LLMs, retrieval-augmented generation, embeddings, vector search, or knowledge graphs, and how did you address accuracy, traceability, human review, and data protection?
Describe a production-grade data pipeline you designed or supported. How did you handle ingestion, transformation, missing or duplicate records, validation, data lineage, and scalability? • How would you monitor a deployed machine-learning model for drift, changing data quality, false positives, false negatives, and performance regression?