Top Skills:
AiArchitectureAwsBig DataBigqueryBusiness IntelligenceCi/cdCloudCloud ComputingComplianceCredit RiskCustomer InsightData AnalyticsData GovernanceData ManagementData PipelineData QualityData VisualizationDeep LearningFinancial DataFutureGcpGitGoogle CloudGoogle Cloud PlatformHadoopLinux Operating SystemMitigationNlpPipelinePythonReportingRisk ModelingScriptingSnowflakeSqlTroubleshooting
What You’ll Do
- Apply the knowledge of data characteristics and data supply pattern, develop rules and tracking process to support data quality model.
- Prepare data for analytical use by building data pipelines to gather data from multiple sources and systems.
- Integrate, consolidate, cleanse and structure data for use by our clients in our solutions.
- Perform design, creation, and interpretation of large and highly complex datasets.
- Stay up-to-date with the latest trends and advancements in GCP and related technologies, actively proposing and evaluating new solutions.
- Understand best practices for data management, maintenance, reporting and security and use that knowledge to implement improvements in our solutions.
- Implement security best practices in pipelines and infrastructure.
- Develop and implement data quality checks and troubleshoot data anomalies.
- Provide guidance and mentorship to junior data engineers.
- Review dataset implementations performed by junior data engineers.
- BS degree in a STEM major or equivalent discipline; Master’s Degree strongly preferred
- 5+ years of experience as a data engineer or related role
- Cloud certification strongly preferred
- Advanced skills using programming languages such as Python or SQL and intermediate level experience with scripting languages
- Intermediate level understanding and experience with Google Cloud Platforms and overall cloud computing concepts, as well as basic knowledge of other cloud environments
- Experience building and maintaining moderately-complex data pipelines, troubleshooting issues, transforming and entering data into a data pipeline in order for the content to be digested and usable for future projects
- Experience designing and implementing moderately complex data models and experience enabling optimization to improve performance
- Demonstrates advanced Git usage and CI/CD integration skills
- Master’s degree in a related field is a strong plus
- Exposure in Vertex AI, GCP AI/ML services (AutoML, BigQuery ML, Cloud Run, etc.) or a similar cloud technology
- Strong foundational skills in Linux Operating System.
- Understanding of NLP, deep learning, and generative architectures (Transformers, Diffusion Models, etc.)
- Background in credit risk, financial data analytics or risk modeling.
- Experience working with large datasets on a big data platform (e.g., Google Cloud, AWS, Snowflake, Hadoop)
- Experience in Business Intelligence, data visualization, and customer insights
- generation.
- Familiarity with data governance, model bias mitigation, and regulatory frameworks (GDPR, AI Act, SEC compliance).
- Experience with MLOps practices, model monitoring, and CI/CD for AI workflows.
- Knowledge of prompt tuning, fine-tuning, and parameter-efficient methods (LoRA, PEFT).
- Hands-on experience with RAG, multi-modal AI, and hybrid AI architectures.
- Contributions to the AI community through publications, open-source projects, or conference presentations.
Generative AI Engineer
Equifax
linkedin
Atlanta, GA
5-10 years Not Disclosed
Full time
16 April 2026
Top Skills:
AiArchitectureAwsBig DataBigqueryBusiness IntelligenceCi/cdCloudCloud ComputingComplianceCredit RiskCustomer InsightData AnalyticsData GovernanceData ManagementData PipelineData QualityData VisualizationDeep LearningFinancial DataFutureGcpGitGoogle CloudGoogle Cloud PlatformHadoopLinux Operating SystemMitigationNlpPipelinePythonReportingRisk ModelingScriptingSnowflakeSqlTroubleshooting
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Job Description
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What You’ll Do
- Apply the knowledge of data characteristics and data supply pattern, develop rules and tracking process to support data quality model.
- Prepare data for analytical use by building data pipelines to gather data from multiple sources and systems.
- Integrate, consolidate, cleanse and structure data for use by our clients in our solutions.
- Perform design, creation, and interpretation of large and highly complex datasets.
- Stay up-to-date with the latest trends and advancements in GCP and related technologies, actively proposing and evaluating new solutions.
- Understand best practices for data management, maintenance, reporting and security and use that knowledge to implement improvements in our solutions.
- Implement security best practices in pipelines and infrastructure.
- Develop and implement data quality checks and troubleshoot data anomalies.
- Provide guidance and mentorship to junior data engineers.
- Review dataset implementations performed by junior data engineers.
- BS degree in a STEM major or equivalent discipline; Master’s Degree strongly preferred
- 5+ years of experience as a data engineer or related role
- Cloud certification strongly preferred
- Advanced skills using programming languages such as Python or SQL and intermediate level experience with scripting languages
- Intermediate level understanding and experience with Google Cloud Platforms and overall cloud computing concepts, as well as basic knowledge of other cloud environments
- Experience building and maintaining moderately-complex data pipelines, troubleshooting issues, transforming and entering data into a data pipeline in order for the content to be digested and usable for future projects
- Experience designing and implementing moderately complex data models and experience enabling optimization to improve performance
- Demonstrates advanced Git usage and CI/CD integration skills
- Master’s degree in a related field is a strong plus
- Exposure in Vertex AI, GCP AI/ML services (AutoML, BigQuery ML, Cloud Run, etc.) or a similar cloud technology
- Strong foundational skills in Linux Operating System.
- Understanding of NLP, deep learning, and generative architectures (Transformers, Diffusion Models, etc.)
- Background in credit risk, financial data analytics or risk modeling.
- Experience working with large datasets on a big data platform (e.g., Google Cloud, AWS, Snowflake, Hadoop)
- Experience in Business Intelligence, data visualization, and customer insights
- generation.
- Familiarity with data governance, model bias mitigation, and regulatory frameworks (GDPR, AI Act, SEC compliance).
- Experience with MLOps practices, model monitoring, and CI/CD for AI workflows.
- Knowledge of prompt tuning, fine-tuning, and parameter-efficient methods (LoRA, PEFT).
- Hands-on experience with RAG, multi-modal AI, and hybrid AI architectures.
- Contributions to the AI community through publications, open-source projects, or conference presentations.