Data and AI Engineer

Prophecy Technologies Corp; Northern (KY); On-site; USD 120,000 - 160,000; Full time

Job summary

Prophecy Technologies Corp is seeking a Data and AI Engineer to develop data, analytics, and AI solutions that help engineering teams identify trends, investigate product issues, and accelerate technical problem solving. The role blends data engineering, ML, software development, and technical communication to transform multi-source data into reliable datasets and insights.

The ideal candidate has 8+ years in engineering data, analytics, and AI, with experience in automotive/manufacturing

Qualifications

• 8+ years of experience in Engineering Data, Analytics, and AI.
• Experience in automotive, manufacturing, mobility, product engineering, quality, reliability, or operations analytics.
• Experience with telemetry, diagnostic, test, warranty, manufacturing, or other engineering 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 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.

Responsibilities

• 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.
• 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.
• 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.

Skills

Python

SQL

Data Engineering

Machine Learning

CI/CD

Cloud Platforms

LLMs

Data Analytics

Communication

Teamwork

Tools

Airflow

Job description

The Data and AI Engineer will develop data, analytics, and artificial-intelligence solutions to 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, working with engineering and business stakeholders to transform complex, multi-source information into reliable datasets, useful analytical models, and clear technical insights.

Key Responsibilities:

• 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.
• 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.
• 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.

Required 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.