AI/ML Engineer

Role: AI/ML Engineer

Senior AI/ML Engineer responsible for designing, building, and deploying AI-driven data reconciliation and automation solutions within a large-scale data migration program.

Responsibilities

• Develop AI/ML pipelines for anomaly detection, automated validation workflows, and AI-assisted data mapping to improve data quality and accelerate migration.
• Build automated, auditable reconciliation to eliminate manual row- and aggregate-level validation across multi-terabyte datasets.
• Translate stakeholder control scenarios (e.g., finance/actuarial/risk) into automated validation logic, acceptance criteria, and agile backlog items.
• Collaborate with technical and business stakeholders; deliver executive-level insights via dashboards and reporting.
• Monitor model performance, manage model drift, and ensure auditability in regulated environments.
• Mentor team members and support knowledge transfer to build internal AI capabilities.

Requirements (Minimum)

• 6+ years: Applied AI/ML pipeline development and deployment for large-scale data reconciliation; production experience with anomaly detection, root-cause analysis, and exception classification using PyTorch, Scikit-learn, and Azure Machine Learning in regulated financial/government environments.
• 6+ years: Azure data platform engineering including Azure Databricks, Azure Data Factory, Azure Synapse Analytics, and Delta Lake.
• 10+ years: Advanced T-SQL and PL/SQL across SQL Server and Oracle (stored procedures, partition switching, columnstore indexing, and query optimization for sub-second ETL/dashboard responsiveness).
• 6+ years: Rule-based exception classification pipelines and prioritized work queue construction.
• 6+ years: Experience translating 30+ stakeholder control scenarios into automated validation logic and acceptance criteria; agile backlog experience.
• 4+ years: Cloud-native ingestion pipeline engineering with Azure Data Factory, Azure Service Bus, and Azure Functions; schema validation and data lineage via Azure Purview.
• 4+ years: Containerized microservice deployment with Docker, AKS, and Git-based CI/CD.
• 4+ years: Production model monitoring and drift detection using Azure Monitor (including custom drift detectors); MLflow tracking and ensemble tuning (gradient boosting).

Nice-to-have / Additional Signals

• Experience sustaining statistical power of validation models across evolving data volumes and product mixes.
• Demonstrated ability to mentor and lead knowledge transfer within the team.