M
Senior Machine Learning / AI Engineer
My3Tech Inc
4 hours ago
Full-time
On-site
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 worker’s expense. Description of Services
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. Candidate Skills and Qualifications
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.
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 worker’s expense. Description of Services
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. Candidate Skills and Qualifications
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.