Pay Rate Range: $ 43.18 - 50.00/hr.
Role: Data and AI Engineer — Automotive Engineering Analytics
Job Description:Must Have Skills:
· 8+ years of experience working with Engineering Data, Analytics, and Artificial Intelligence.
· Experience in automotive, manufacturing, mobility, product engineering, quality, reliability, or operations analytics.
· Experience with telemetry, diagnostic, test, warranty, manufacturing, or other engineering-related data.
· Experience with cloud data platforms, distributed processing, or orchestration tools.
· Experience with large-language models, retrieval-augmented generation, embeddings, vector search, or knowledge graphs.
· Experience processing technical documents, source code, diagrams, or other semi-structured content.
· Experience developing dashboards, web applications, or self-service analytical tools.
· Experience with automated testing, CI/CD, data-quality frameworks, or model-regression suites.
· Professional experience with Python or another programming language used for data and analytics.
· Strong SQL skills and experience working with relational or analytical data platforms.
· Experience cleaning, transforming, joining, validating, and analyzing data from multiple sources.
· Familiarity with statistical methods, machine-learning concepts, and model evaluation.
· Experience with APIs, notebooks, cloud platforms, data pipelines, or analytical applications.
· Ability to understand technical documentation and collaborate with subject-matter experts.
· Strong written and verbal communication skills.
· Ability to work independently while contributing effectively to a cross-functional team.
Roles & Responsibilities
The Data and AI Engineer will develop data, analytics, and artificial-intelligence solutions that help engineering teams identify trends, investigate product issues, improve decision-making, and accelerate technical problem solving.
This role combines data engineering, statistical analysis, machine learning, software development, and technical communication. The successful candidate will work with engineering and business stakeholders to transform complex, multi-source information into reliable datasets, useful analytical models, and clear technical insights.
Responsibilities:
Data engineering and preparation
· Collect, integrate, and organize structured and unstructured data from approved enterprise sources.
· Build repeatable workflows for data ingestion, transformation, validation, and quality monitoring.
· Clean, normalize, and prepare data for analytics, reporting, and machine-learning applications.
· Identify missing, inconsistent, duplicated, or anomalous records and document their impact.
· Maintain data lineage, version control, documentation, and reproducibility across analytical workflows.
Analytics and machine learning
· Apply statistical analysis, correlation techniques, and machine-learning methods to identify meaningful patterns and trends.
· Develop, test, and evaluate supervised, unsupervised, and hybrid analytical approaches.
· Define appropriate metrics and validation methods for model and workflow performance.
· Investigate false positives, false negatives, data-quality issues, model drift, and other analytical risks.
Artificial intelligence and knowledge systems
· Support the development of AI-enabled applications that combine data with approved technical or business knowledge.
· Evaluate large-language-model, retrieval, search, summarization, and knowledge-representation approaches where appropriate.
· Design prompts, evaluation criteria, and validation processes for AI-generated outputs.
· Improve the traceability, consistency, accuracy, and usability of AI-assisted results.
· Apply responsible-AI practices, including human review, access controls, data protection, and transparent documentation.
· Collaborate with software and platform engineers to move prototypes toward maintainable solutions.
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?
| Skills: | | Category | Name | Required | Importance | Experience |
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| SkillCategoryTest1_MN | Digital : Artificial Intelligence(AI) | Yes | 1 | >7 years |
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