Machine Learning / AI Engineer

Machine Learning / AI Engineer

Location: Austin, Texas - Hybrid or Remote

**This role can be hybrid or remote**

JOB DESCRIPTION

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.

Minimum Requirements

This role is for a Machine Learning / AI Engineer with applied research experience in LLM pipeline development, model

evaluation, and intelligent automation.

Years Skill/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 microservice 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.

Preferred Requirements

Years Skill/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.