Data/Systems Engineer – AI & Analytics Systems

Quality Analytics; Northern (KY); Hybrid; USD 80,000 - 90,000; Full time

Job summary

Quality Analytics Associates (QAA) is seeking an engineer to lead data integration, systems engineering, and AI/analytics infrastructure. You will develop data pipelines, evidence transformations, interfaces, and architectures to support analytical-model execution.

The role emphasizes implementing data provenance, model-traceability, and robust data environments for statistical and AI/ML workflows. Collaboration with Data Scientist/ML Lead is expected to deliver integrated solutions.

Qualifications

• Bachelor's degree in computer science, software engineering, data engineering, systems engineering, information systems, or a related technical discipline; or equivalent years' experience.
• Experience designing or implementing data-intensive software, analytics platforms, data pipelines, or integrated information systems.
• Experience with Python and SQL and with modern data-processing or application-development technologies.
• Experience integrating structured and/or unstructured data from multiple systems or sources; data transformation, schema design, APIs, databases, and data pipelines.
• Working knowledge of AI/ML development workflows and the data/infrastructure requirements for model training, evaluation, deployment, or integration.
• Ability to develop and maintain technical architecture, interface, and system documentation.

Responsibilities

• Design and implement data pipelines supporting ingestion and integration of various evidence types.
• Develop processes for data processes for statistical and AI/ML analysis.
• Design technical architectures and presentation.
• Develop and maintain data schemas, evidence models, interfaces, APIs, and integration components.
• Implement mechanisms that preserve data provenance and source-to-output/model traceability.
• Engineer analytical data sets and technical environments supporting model development, testing, evaluation, and demonstration.
• Integrate statistical and machine-learning models developed by the Data Scientist/ML Lead.
• Support implementation of model outputs, explainable indicators, uncertainty information, and supporting evidence within interfaces.
• Support engineering, configuration, integration, testing, troubleshooting, and technical documentation.
• Evaluate system and data dependencies, integration constraints, interoperability considerations, and resource requirements.
• Support cybersecurity, data governance, access-control, authorized-use, and technical risk considerations applicable to the environment.
• Develop or support demonstration environments for evaluation.
• Document technical architecture, data flows, interfaces, dependencies, limitations, and maturation requirements.
• Support transition planning.

Skills

Python

SQL

Data pipelines

APIs

Data transformation

Schema design

Data integration

AI/ML workflows

Education

Bachelor's degree or equivalent experience

Bachelor's degree in CS/SE/Data Eng

Tools

REST APIs

Job description

Summary:Quality Analytics Associates (QAA) is accepting resumes for qualified individual(s) to provide the primary data integration, systems engineering, AI/analytics infrastructure, and engineering support. The engineer will develop data pipelines, evidence transformations, system interfaces, technical architecture, and components needed to support analytical-model execution.

Salary:$80,000 - $90,000 (negotiable)

Responsibilities

• Design and implement data pipelines supporting ingestion and integration of various evidence types.
• Develop processes for data processes for statistical and AI/ML analysis.
• Design technical architectures and presentation.
• Develop and maintain data schemas, evidence models, interfaces, APIs, and integration components.
• Implement mechanisms that preserve data provenance and source-to-output/model traceability.
• Engineer analytical data sets and technical environments supporting model development, testing, evaluation, and demonstration.
• Integrate statistical and machine-learning models developed by the Data Scientist/ML Lead.
• Support implementation of model outputs, explainable indicators, uncertainty information, and supporting evidence within interfaces.
• Support engineering, configuration, integration, testing, troubleshooting, and technical documentation.
• Evaluate system and data dependencies, integration constraints, interoperability considerations, and resource requirements.
• Support cybersecurity, data governance, access-control, authorized-use, and technical risk considerations applicable to the environment.
• Develop or support demonstration environments for evaluation.
• Document technical architecture, data flows, interfaces, dependencies, limitations, and maturation requirements.
• Support transition planning.

Required Qualifications

• Bachelor's degree in computer science, software engineering, data engineering, systems engineering, information systems, computer engineering, or a related technical discipline. OR equivalent years' experience
• Demonstrated experience designing or implementing data-intensive software, analytics platforms, data pipelines, or integrated information systems.
• Experience with Python and SQL and with modern data-processing or application-development technologies.
• Experience integrating structured and/or unstructured data from multiple systems or sources.Experience with data transformation, schema design, APIs, databases, and data pipelines.
• Working knowledge of AI/ML development workflows and the data/infrastructure requirements associated with model training, evaluation, deployment, or integration.Experience supporting prototype or research-system development.
• Ability to develop and maintain technical architecture, interface, and system documentation.

Preferred Qualifications

• Master's degree in computer science, engineering, data science, AI/ML, or a related discipline.
• Experience engineering systems that incorporate machine-learning or AI models.
• Experience with MLOps, model-serving architectures, feature pipelines, experiment/model management, or AI/ML lifecycle tooling.