M
Data Conversion Specialist (Senior ML/AI Engineer)
My3Tech Inc
4 hours ago
Full-time
On-site
Data Conversion Specialist (Senior ML/AI Engineer)
ERS is seeking a Senior ML/AI Engineer (12+ years of overall experience) to design and maintain AI-driven data pipelines for the RISE data migration program. This includes anomaly detection, exception classification, field mapping tooling, and real-time quality dashboards that support conversion specialists and program leadership. The role requires building auditable, lightweight automation on Azure that integrates with the Landing Zone to CDR ETL process, applying LLM evaluation frameworks to validate reconciliation outputs, and producing clear technical documentation and knowledge transfer to ensure ERS staff can operate and extend the tools independently. Candidates must have deep production experience with AI-driven data reconciliation in regulated environments (SOX, PCI-DSS, HIPAA) and the ability to communicate technical findings to finance, actuarial, risk, and program leadership stakeholders. Candidate Skills and Qualifications
Required Experience: 4+ Years: Data conversion development for large-scale pension administration system modernization programs; source-to-target mapping, ETL execution, exception handling, and validation across multi-billion-record legacy data sets. 4+ Years: SSIS package development and BIML scripting for automated, repeatable data extraction, transformation, and load processes across legacy pension and structured relational data environments. 4+ Years: SQL Server and T-SQL development including complex stored procedures, views, indexing strategies, query optimization, and execution plan analysis to support high-volume ETL and reconciliation workflows. 3+ Years: C# and.NET development for data conversion automation scripts, SSIS custom components, and backend data processing logic within a structured SDLC environment. 3+ Years: Legacy system data migration experience including source environments, multi-environment extract and load processes, participation in mock runs, UAT cycles, and production cutover activities. 2+ Years: Data reconciliation processes including record count validation, key field comparison, exception reporting, authorization and exclusion logic, and validation within a large-scale pension or benefits modernization platform. Preferred Experience: 3+ Years: Azure Data Factory, PySpark, or Apache Airflow pipeline development for ELT/ETL orchestration, incremental loading strategies, and data quality validation in cloud or hybrid data environments. 2+ Years: Power BI or SSRS dashboard and report development including ETL job completion tracking, exception rate reporting, and data quality KPI visualization for program leadership and QA teams. 2+ Years: Cloud data warehouse development using dbt Core for staging, intermediate, and reporting layer transformations with incremental load strategies (SnowPro Core or Microsoft Fabric certification a plus). 2+ Years: Defect triage and root cause analysis using SQL scripts and SSIS debugging techniques; cross-functional collaboration with QA, functional testers, and business analysts during mock loads and UAT resolution cycles.
ERS is seeking a Senior ML/AI Engineer (12+ years of overall experience) to design and maintain AI-driven data pipelines for the RISE data migration program. This includes anomaly detection, exception classification, field mapping tooling, and real-time quality dashboards that support conversion specialists and program leadership. The role requires building auditable, lightweight automation on Azure that integrates with the Landing Zone to CDR ETL process, applying LLM evaluation frameworks to validate reconciliation outputs, and producing clear technical documentation and knowledge transfer to ensure ERS staff can operate and extend the tools independently. Candidates must have deep production experience with AI-driven data reconciliation in regulated environments (SOX, PCI-DSS, HIPAA) and the ability to communicate technical findings to finance, actuarial, risk, and program leadership stakeholders. Candidate Skills and Qualifications
Required Experience: 4+ Years: Data conversion development for large-scale pension administration system modernization programs; source-to-target mapping, ETL execution, exception handling, and validation across multi-billion-record legacy data sets. 4+ Years: SSIS package development and BIML scripting for automated, repeatable data extraction, transformation, and load processes across legacy pension and structured relational data environments. 4+ Years: SQL Server and T-SQL development including complex stored procedures, views, indexing strategies, query optimization, and execution plan analysis to support high-volume ETL and reconciliation workflows. 3+ Years: C# and.NET development for data conversion automation scripts, SSIS custom components, and backend data processing logic within a structured SDLC environment. 3+ Years: Legacy system data migration experience including source environments, multi-environment extract and load processes, participation in mock runs, UAT cycles, and production cutover activities. 2+ Years: Data reconciliation processes including record count validation, key field comparison, exception reporting, authorization and exclusion logic, and validation within a large-scale pension or benefits modernization platform. Preferred Experience: 3+ Years: Azure Data Factory, PySpark, or Apache Airflow pipeline development for ELT/ETL orchestration, incremental loading strategies, and data quality validation in cloud or hybrid data environments. 2+ Years: Power BI or SSRS dashboard and report development including ETL job completion tracking, exception rate reporting, and data quality KPI visualization for program leadership and QA teams. 2+ Years: Cloud data warehouse development using dbt Core for staging, intermediate, and reporting layer transformations with incremental load strategies (SnowPro Core or Microsoft Fabric certification a plus). 2+ Years: Defect triage and root cause analysis using SQL scripts and SSIS debugging techniques; cross-functional collaboration with QA, functional testers, and business analysts during mock loads and UAT resolution cycles.