AI/ML Engineer

JOB TITLE: AI/ML Engineer LOCATION: Austin, TX – Remote or Hybri d JOB DESCRIPTION/MINIMUM REQUIREMENT S:This role is for a senior AI/ML Engineer responsible for designing, building, and deploying AI-driven data reconciliation and automation solutions within a large-scale data migration program. The engineer will develop anomaly detection pipelines, automated validation workflows, and AI-assisted data mapping tools to improve data quality and accelerate migration processes. The role requires hands-on expertise with Azure-based AI/ML platforms and the ability to monitor model performance, manage drift, and ensure auditability in regulated environments. The candidate will collaborate with technical and business stakeholders, translate requirements into intelligent automation logic, and deliver executive-level insights through dashboards and reporting. Additionally, the engineer will mentor team members and support knowledge transfer to build internal AI capabilitie s. Minimum Requiremen t s:Ye arsSkills/Experie nc e6+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 environme nt s 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 data set s 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 work lo ads 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 i tems 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-base d 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 produ ct mixes