| Location | Austin, TX |
Senior AI/ML Engineer:
Total Required Experience in Years:15%2B Years
Mode of Work:Remote / Hybrid Austin, Texas
Seeking an experienced Senior AI/ML Engineer to support a large-scale enterprise data modernization and migration initiative. The consultant will be responsible for designing, developing, deploying, and maintaining AI-driven automation and data reconciliation solutions that improve data quality, accelerate migration efforts, and reduce manual validation processes.
The ideal candidate will possess strong expertise in Azure AI/ML technologies, anomaly detection, machine learning pipelines, cloud-native data engineering, automated validation frameworks, and model monitoring within regulated government or financial environments.
Design and deploy AI/ML solutions for enterprise data migration and reconciliation programs.
Build anomaly detection, exception classification, and root-cause analysis models.
Develop AI-assisted data mapping and validation automation tools.
Create automated reconciliation workflows eliminating manual validation activities.
Design and maintain Azure Machine Learning pipelines and deployment frameworks.
Develop cloud-native data ingestion and processing solutions.
Build automated validation rules and exception management frameworks.
Collaborate with business stakeholders to translate requirements into AI-driven solutions.
Develop dashboards and reporting solutions for executive-level insights.
Monitor model performance, accuracy, drift, and operational effectiveness.
Implement MLflow tracking and model lifecycle management processes.
Design data lineage, governance, and auditability frameworks.
Support CI/CD automation for AI/ML and data engineering workloads.
Mentor technical team members and provide knowledge transfer.
Ensure compliance with governance, security, and regulatory requirements.
Azure Machine Learning
Azure Databricks
Azure Data Factory (ADF)
Azure Synapse Analytics
Delta Lake
PyTorch
Scikit-learn
AI/ML Model Development
Anomaly Detection
Root Cause Analysis
Exception Classification
Data Reconciliation
Machine Learning Pipelines
MLOps
MLflow
Azure Monitor
Model Drift Detection
Azure Purview
Azure Functions
Azure Service Bus
Docker
Azure Kubernetes Service (AKS)
Git-Based CI/CD
Data Lineage Management
T-SQL
PL/SQL
SQL Server
Oracle Database
Query Optimization
Stored Procedures
Partition Switching
Columnstore Indexing
Dashboard Development
Agile Methodologies
Requirements Analysis
Technical Leadership
Stakeholder Management
6%2B years of AI/ML pipeline development and deployment experience.
6%2B years of Azure data platform engineering experience.
10%2B years of advanced SQL Server and Oracle database development experience.
6%2B years of experience developing automated exception classification and validation frameworks.
4%2B years of cloud-native data ingestion and microservices development experience.
4%2B years of model monitoring, drift detection, and ML lifecycle management experience.
Experience working within regulated government, financial, pension, or enterprise environments.
Experience translating business controls and validation requirements into automated AI-driven workflows.
Experience supporting pension modernization programs.
Experience with large-scale enterprise data migration initiatives.
Experience with regulated financial services environments.
Experience building explainable AI and governance-focused AI solutions.
Experience supporting executive reporting and operational analytics.
Experience mentoring AI/ML engineering teams.
AI/ML Models
Anomaly Detection Frameworks
Data Reconciliation Pipelines
Exception Classification Engines
Automated Validation Workflows
Azure ML Pipelines
Model Monitoring Dashboards
Drift Detection Reports
Data Lineage Documentation
Technical Design Documents
Executive Reporting Dashboards
CI/CD Deployment Pipelines
Knowledge Transfer Documentation
Operational Runbooks
Bachelors Degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Information Systems, or related field (or equivalent experience).