Senior Machine Learning AI Engineer

Job Reference/ID: ERS (LM) Machine Learning / AI Engineer
Client: Employees Retirement System of Texas
Employment Type: Full-Time (Contract)
Work Location: Austin Texas Hybrid or Remote

• Note: All travel per diem parking and/or living expenses shall be at the workers expense.

The client is seeking a Senior Machine Learning / AI Engineer with over 12 years of production experience to design build and maintain an AI-driven data reconciliation and analytics pipeline for the RISE data migration program. Operating within a highly regulated Azure environment (SOX PCI-DSS HIPAA) the ideal candidate will develop auditable anomaly detection exception classification workflows and LLM-evaluation frameworks to accelerate data conversion and provide real-time quality metrics for leadership.

Beyond technical deployment this role requires excellent communication skills to translate complex AI outputs for finance risk and program stakeholders alongside a commitment to providing comprehensive technical documentation and knowledge transfer to embedded staff.

Mandatory Submission Requirement:

• Resume and Photo ID required: For the safety and security of our clients and their systems any candidate resume submitted must also include a photo ID of the candidate placed at the beginning of the resume. Any candidate resume submitted without a photo ID will be eliminated from potential selection for the role.

Required Experience:

• 12 Years: Production experience in Machine Learning / AI Engineering.
• 10 Years: 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 Years: 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 Years: 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.
• 6 Years: 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 Years: 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 microservice deployment via Docker AKS and Git-based CI/CD.
• 4 Years: 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.
• General: Applied research experience in LLM pipeline development model evaluation and intelligent automation.

Preferred Experience:

• 4 Years: 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 Years: 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 Years: 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.