US
We are seeking a Senior AI/ML Developer for a hybrid position in Austin, Texas; we may offer a remote option for the right candidate.
This role is for a Machine Learning / AI Engineer with applied research experience in LLM pipeline development, model evaluation, and intelligent automation. The role is technical and requires the Worker to serve as the AI capability layer for the data migration delivery team on the RISE program. The Worker does not need prior pension administration experience; the Technical Architect and ERS conversion specialists will provide domain context. The Worker will design, build, and deploy AI/ML tooling that accelerates and augments the work of conversion specialists-compressing manual review cycles, surfacing data anomalies earlier, and enabling intelligent automation of repeatable reconciliation and mapping tasks.
The Worker must demonstrate direct production experience designing automated, auditable reconciliation workflows using Azure Databricks, Azure Data Factory, and Azure Machine Learning, with a proven track record of surfacing data integrity issues before they impact downstream reporting. The Worker must have demonstrated ability to translate stakeholder control scenarios into automated validation logic, manage model drift in production environments, and communicate AI pipeline findings to finance, actuarial, and risk audiences through executive-level dashboards. The Worker will follow all organizational Standard Operating Procedures related to deliverable approvals, reviews, and associated workflows.
The Worker will rely on their senior engineering experience and production delivery track record to independently architect and execute AI pipeline deliverables, mentor team members, and contribute to knowledge transfer activities that build ERS staff capability in Azure-based AI reconciliation tooling. The Worker is expected to demonstrate a high degree of technical rigor, clean architecture discipline, and cross-functional stakeholder communication.
The Worker will be expected to demonstrate their knowledge and skills in Azure-based AI/ML pipeline architecture, automated reconciliation framework design, anomaly detection model development, and production model monitoring during the interview process.
Must have Advanced T-SQL and PL/SQL development across SQL Server and Oracle, including stored procedures, partition switching, columnstore indexing, and query optimization sustaining sub-second query response for high-volume ETL and dashboard workloads. |
Professional Expectations:
- Attend all meetings, meet delivery deadlines, and be available during ERS office hours.
- Logs in and remains on agency Jabber during work hours.
- Attends remote meetings with camera on unless previously arranged to be off.
- Coordinates leave and vacation with ERS lead.
- Must dress appropriately for a business/business casual environment.
- Communicates respectfully and works harmoniously with all co-workers, customers, and vendors.
- Provides exceptional customer service.
- Is flexible; able to work under pressure; able to adapt to change; and able to work on multiple problems and tasks.
- Takes initiative to prevent and solve problems
Years | Skills/Experience |
6 | Applied AI/ML pipeline development and deployment for large-scale data reconciliation programs; production experience building anomaly-detection, root-cause analysis, and exception classification models using PyTorch, Scikit-learn, and Azure Machine Learning in regulated financial or government environments |
6 | Azure data platform engineering including Azure Databricks, Azure Data Factory, Azure Synapse Analytics, and Delta Lake; demonstrated ability to design automated, auditable reconciliation workflows eliminating manual row- and aggregate-level validation across multi-terabyte datasets |
10 | Advanced T-SQL and PL/SQL development across SQL Server and Oracle, including stored procedures, partition switching, columnstore indexing, and query optimization sustaining sub-second query response for high-volume ETL and dashboard workloads |
6 | Rule-based exception classification pipelines and prioritized work queue construction; experience translating 30 stakeholder control scenarios (finance, actuarial, risk) into automated validation logic, acceptance criteria, and agile backlog items |
4 | Cloud-native ingestion pipeline engineering with Azure Data Factory, Azure Service Bus, and Azure Functions; schema validation, data lineage management with Azure Purview, and containerized micro-service deployment via Docker, AKS, and Git-based CI/CD |
4 | Production model monitoring and drift detection using Azure Monitor metrics and custom drift detectors; MLflow experiment tracking and gradient-boosting ensemble tuning ensuring validation models retain statistical power across evolving data volumes and product mixes |
Master's degree in Information Technology, Science, Computer Science, or equivalent |
Preferred:
Years | Skills/Experience |
4 | Continuous data quality enforcement using Great Expectations and parameterized pytest suites; experience validating 100 reconciliation rules on synthetic and production samples with automated regression coverage for SOX, PCI-DSS, or HIPAA-regulated audit environments |
3 | Legacy system data migration experience involving COBOL or mainframe source environments (AWS Glue, Redshift, or equivalent); aggregate validation checks, tolerance-threshold variance surfacing, and actuarial or regulatory sign-off workflows for government or healthcare modernization programs |
3 | Azure Purview data lineage and metadata management; Delta Lake compaction, ACID semantics, and Parquet optimization for downstream analytics; Azure Key Vault managed identity integration for encryption-in-transit and at-rest compliance across reconciliation artifacts |