Quality Data & AI Agent Engineer

Swj Technology•Vance, AL

1d•Onsite

About The Position

SWJ Technologie is seeking a Quality Data & AI Agent Engineer for a client in the automotive car-manufacturing industry. The contractor role is based in Vance, Alabama and requires onsite presence. The engineer analyzes manufacturing and quality data to identify trends, detect anomalies, investigate root causes, and drive continuous improvement. The role develops AI agents, automated analytics, and digital workflows that turn complex data into actionable insights, improve quality performance, and support data-driven engineering decisions. The position works with Quality, Production, Engineering, IT, and Data/AI teams to translate operational requirements into scalable digital solutions. It also supports the implementation of data-driven problem-solving and continuous improvement initiatives, and documents analytical methods, AI workflows, data integrations, and solution functionality. Work is normally performed in an office, laboratory, manufacturing-floor, or machine-shop setting, with compliance with relevant safety practices required.

Requirements

Bachelor's degree in Data Science, Computer Science, Information Technology, Engineering, Statistics, or a related technical field; relevant professional experience in data analytics, AI development, software engineering, or manufacturing quality may be considered in place of a degree.
Experience working with complex datasets and developing data-driven solutions.
Understanding of manufacturing processes, quality management, and industrial data environments.
Experience translating business or engineering requirements into technical solutions.
Proficiency in Python and SQL for data processing, analysis, and automation.
Experience with data analytics, statistical analysis, and data visualization.
Ability to develop dashboards, reports, and analytical solutions using Power BI or comparable tools.
Knowledge of AI agents, agentic AI concepts, large language models (LLMs), and prompt engineering.
Experience integrating data sources, APIs, and software tools to support automated workflows.
Strong analytical and problem-solving skills, including identifying patterns, investigating anomalies, and evaluating potential root causes.
Ability to interpret complex data and communicate findings clearly and actionably.
Attention to data quality, analytical accuracy, and validation of AI-generated results.
Strong collaboration skills and ability to work with cross-functional technical and operational teams.
Ability to sit, stand, reach, kneel, bend, and lift up to 25 lbs.
Ability to understand and comply with relevant safety practices.

Nice To Haves

Experience with Microsoft Fabric, Azure AI, Microsoft Copilot Studio, or similar data and AI platforms.
Knowledge of retrieval-augmented generation (RAG), AI tool integration, and enterprise AI architectures.
Experience developing AI-powered applications, autonomous agents, or multi-step automated workflows.
Knowledge of machine learning techniques, anomaly detection, and predictive analytics.
Experience with manufacturing execution systems (MES), quality management systems (QMS), enterprise resource planning (ERP) systems, or industrial databases.
Familiarity with Root Cause Analysis (RCA), Pareto analysis, Statistical Process Control (SPC), process capability, and quality KPIs.
Understanding of defect and failure analysis, manufacturing process optimization, and continuous improvement methodologies.
Experience automating engineering reports, quality investigations, and operational decision-support processes.
Knowledge of data governance, model validation, AI reliability, and traceability requirements.

Responsibilities

Analyze manufacturing and quality datasets to identify defect trends, correlations, anomalies, and potential root causes.
Develop and maintain dashboards, data models, and automated analytics solutions to monitor quality performance and manufacturing processes.
Design, develop, and implement AI agents that analyze data, answer quality-related questions, generate reports, and support engineering workflows.
Integrate AI agents with manufacturing databases, quality management systems, technical documentation, and other enterprise data sources.
Develop and automate workflows for quality KPI monitoring, defect and failure analysis, root-cause investigation, reporting, and early-warning detection.
Apply statistical methods and machine learning techniques to identify patterns, predict potential quality issues, and uncover improvement opportunities.
Collaborate with Quality, Production, Engineering, IT, and Data/AI teams to translate operational requirements into scalable digital solutions.
Validate AI-generated results and analytical outputs for accuracy, reliability, traceability, and relevance to manufacturing and quality processes.
Support data-driven problem-solving methods and continuous improvement initiatives.
Develop reusable analytics components, AI agents, and automated workflows that can be scaled across production areas and quality applications.
Document analytical methods, AI workflows, data integrations, and solution functionality.