Artificial Intelligence/Machine Learning Engineer

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 in nature and requires the Worker to function as the AI

capability layer for Provaliant’s lean data migration delivery team on the RISE program. The Worker does not require prior pension administration experience; domain context will be provided by the Technical Architect and ERS

conversion specialists. The Worker’s contribution is to 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. A high degree of technical rigor, clean architecture discipline, and cross-functional stakeholder communication is expected.

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.

Functional Responsibilities:  

ERS is seeking a Machine Learning / AI Engineer with 12 years of senior production experience and delivers AI-driven data reconciliation and analytics pipeline solutions in regulated environments. The Worker will design, build, and

maintain the AI automation layer for the RISE data migration program, developing auditable anomaly detection pipelines, exception classification workflows, and real-time quality dashboards that accelerate conversion specialist

throughput and provide ERS program leadership with continuous visibility into migration integrity.

The worker will be responsible for:

• Design and deploy ML-based anomaly detection pipelines layered on the Landing Zone to CDR ETL process, providing early-cycle flagging of data discrepancies before they propagate downstream

• Build AI-assisted field mapping and classification tooling to accelerate source-to-target schema mapping across CDR cycles, enabling conversion specialists to apply prior resolution decisions consistently across

subsequent cycles

• Develop automated data quality scoring pipelines producing per-table and per-CDR-cycle quality metrics, providing QA and program leadership with real-time visibility into migration health

• Apply LLM evaluation methodology and judge-model scoring frameworks to assess and validate AI-assisted reconciliation outputs for accuracy, consistency, and auditability

• Develop and maintain lightweight, maintainable AI tooling that ERS-embedded staff can understand, operate, and extend following the engagement

• Produce technical documentation of AI pipeline logic, model behavior, and automation design decisions in formats accessible to conversion specialists and program management

• Actively participate in knowledge transfer sessions, helping ERS staff develop literacy in how AI was applied to the migration and what it produced

The Worker should have deep production experience delivering AI-driven data reconciliation frameworks on Azure platforms, with demonstrated ability to build auditable anomaly detection and exception classification pipelines at scale,

manage model performance in regulated environments (SOX, PCI-DSS, HIPAA), and communicate findings clearly to finance, actuarial, risk, and program leadership stakeholders. 

Other Duties and Responsibilities:

• Performs other duties as assigned

WORKER SKILLS AND QUALIFICATIONS

Minimum:

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 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:

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