Position Summary:
The IT Division is responsible for the development and operations of information systems for the State and Federal agencies doing business related to or using information from the administration of motor vehicles and driver licenses.
The Machine Learning (ML) Data Engineer position has core responsibilities for the design, development, deployment, and operational support of machine learning solutions on cloud infrastructure. This includes the full model lifecycle — from data acquisition and dataset preparation through feature engineering, experimentation, model training, validation, production deployment, and ongoing monitoring. Current applications include anomaly detection across high-volume messaging networks, but the scope encompasses any ML capability that strengthens system reliability, operational intelligence, and data-driven decision-making across AAMVA systems.
Essential Duties and Responsibilities:
We are seeking a talented Data Engineer with machine learning experience to join our team. You will design, build, and operationalize ML solutions running on cloud infrastructure (Azure or AWS). You will work across the full model lifecycle: preparing datasets, engineering features, running experiments, deploying models to production, and operating them on cloud infrastructure.
As a detail-oriented professional, you have a strong track record of independently managing projects and driving them to successful completion. Your statistical foundation and engineering discipline enable you to move from exploratory analysis through to production-grade, monitored solutions. You communicate clearly with both technical and non-technical stakeholders — translating model behavior, data constraints, and engineering trade-offs into terms that drive decisions. You operate effectively across the broader IT organization, with sufficient general IT fluency to understand how ML systems interact with infrastructure, security, operations, and business workflows, and you proactively build those connections rather than working in a data silo.
Key responsibilities include:
- Designing and building dataset preparation pipelines — acquiring, cleaning, transforming, and versioning data for ML training and evaluation
- Engineering features that extract meaningful signals from structured and semi-structured data sources (time-series patterns, statistical profiles, categorical encodings)
- Running structured experimentation — testing multiple algorithms against defined scenarios, measuring performance, and documenting findings
- Training, evaluating, and tuning ML models including regression, classification, clustering, anomaly detection, and ensemble methods
- Deploying models to production on cloud infrastructure and building the pipelines that keep them running (retraining, scoring, threshold management)
- Monitoring model performance in production — tracking drift, false positive rates, and detection efficacy over time
- Building and maintaining batch and streaming data pipelines using Synapse, Fabric, Spark, and Event Hubs that feed ML systems
- Writing and optimizing analytical queries (SQL, KQL, PySpark) for data exploration, statistical profiling, and real-time analysis
- Creating validation frameworks — synthetic test data generation, backtesting against historical logs, and shadow-mode evaluation
- Building dashboards and visualizations that communicate model outputs to technical and non-technical stakeholders
- Collaborating with cross-functional teams to identify ML opportunities and translate operational problems into data solutions; communicating findings, trade-offs, and model behavior clearly to technical and non-technical audiences across IT, operations, and leadership
Direct Reports:
None
QUALIFICATIONS
Formal Education:
Bachelor's degree in computer science, data science, statistics, mathematics, or related quantitative field. Equivalent work experience may be substituted
Knowledge, Skills, and Abilities:
Basic Qualifications
- 3–5 years of hands-on experience in data engineering, ML engineering, or applied analytics
- Hands-on cloud platform experience (Azure or AWS) building and deploying data or ML solutions on managed cloud services; specific platform less important than depth of experience
- Working knowledge of statistical foundations: distributions, variance, standard deviation, trend vs. seasonality, hypothesis testing, and how to apply them to real operational data
- Experience with the ML experiment-to-production cycle: dataset preparation, feature engineering, model training, evaluation, and deployment
- Proficiency in Python for data processing, statistical analysis, and ML model development
- Strong SQL skills with understanding of relational database fundamentals: data modeling, query optimization, indexing strategies, and how SQL Server infrastructure supports production workloads (T-SQL, stored procedures, Availability Groups)
- Experience building data pipelines that handle batch and streaming workloads
- Experience with version control systems (Git) and CI/CD practices
- Strong problem-solving skills, attention to detail, and ability to work independently on ambiguous problems
- Strong written and verbal communication skills — able to explain technical findings to non-technical stakeholders and engage productively across IT, operations, and leadership; comfort operating outside the ML silo and contributing to broader technology discussions
Preferred Qualifications
- Experience with time-series analysis, anomaly detection, or statistical process control on operational data
- Familiarity with unsupervised and semi-supervised techniques (isolation forest, clustering, ensemble methods)
- Experience building and managing ML model lifecycle on Azure (MLflow, Fabric ML, Azure ML) or AWS (SageMaker, Glue, Step Functions)
- Familiarity with KQL (Kusto Query Language) for time-series decomposition, log analytics, or real-time data exploration
- Knowledge of data modeling and dimensional modeling concepts
- Experience with synthetic test data generation and model validation frameworks
- Familiarity with operations and monitoring of mission-critical data platforms
Technical Stack
- Core Technologies: Microsoft Fabric, Azure Synapse Analytics, Apache Spark, Delta Lake, Azure Event Hubs
- ML & Analytics: scikit-learn, PySpark ML, statistical modeling, time-series analysis, feature engineering, model validation
- Languages: Python, SQL, PySpark, KQL, C#
- Data Infrastructure: T-SQL, Stored Procedures, SQL Server Availability Groups
- Azure Services: Azure Functions, Azure Data Factory, Azure Key Vault
- Optional: Databricks, Snowflake, Lakehouse Architecture, Azure OpenAI; AWS candidates: equivalent services (SageMaker, Glue, Kinesis, Redshift) are acceptable in place of Azure-specific stack items
- Visualization: Power BI
- Development: Azure DevOps, CI/CD
Disclaimer Statement: The preceding job description has been written to reflect management’s assignment of essential functions. It does not prescribe or restrict the tasks that may be assigned.
AAMVA is an Equal Opportunity Employer/Veterans/Disabled
