Quick Overview
Seniority
Mid Senior
Work mode
On Site
Location
Linthicum Heights, MD, United States
Posted
15 hours ago
SQLAWSETLScikit-learnSnowflakeAirflowApacheAzureBigQueryDatabricksGoogle CloudKafkaPyTorchPythonRESTRedshiftTensorFlowTerraformVaultdbt
Job Description
AI/ML Data Platform Engineer
Location: Linthicum Heights, MD
Work Arrangement: Onsite
Number of Positions: 1
Location: Linthicum Heights, MD
Work Arrangement: Onsite
Number of Positions: 1
Position Overview:
The AI/ML Data Platform Engineer will design, build, and maintain reusable data products, pipelines, APIs, and AI-enabled data services supporting state government analytics and AI/ML initiatives. The role focuses on data integration, real-time streaming, data quality, governance, feature engineering, and ML model integration.
Key Requirements:
- 5+ years of experience in data engineering, software engineering, or ML engineering, including 2+ years in data product or data service design.
- Bachelor's degree in Computer Science, Data Science, Engineering, or a related field; master's degree or PhD preferred.
- Strong proficiency in Python and SQL.
- Experience developing production-quality ETL/ELT pipelines.
- Hands-on experience with AWS, Azure, or Google Cloud Platform data platforms, including Amazon Redshift, BigQuery, Snowflake, Databricks, or equivalent services.
- Experience with Apache Kafka, AWS Kinesis, Google Pub/Sub, Azure Event Hubs, or equivalent streaming technologies.
- Familiarity with Apache Airflow, dbt, Prefect, or similar orchestration tools.
- Understanding of star and snowflake schemas, medallion architecture, and data vault.
- Experience designing REST APIs and working with event-driven architectures.
- Understanding of ML fundamentals, feature engineering, data manipulation, exploratory data analysis, model evaluation, and deployment.
- Familiarity with PyTorch, TensorFlow, or scikit-learn.
- Experience with data quality, pipeline monitoring, troubleshooting, and incident resolution.
- Strong analytical, problem-solving, communication, and collaboration skills.
Preferred Experience:
- Great Expectations, Soda, or dbt tests.
- Apache Atlas, DataHub, or Collibra.
- Feature stores such as Feast or Tecton.
- Integrating ML model outputs into data pipelines and data products.
- Terraform and infrastructure as code.
- Government, public sector, or regulated data environments.
- Data governance, privacy, and security requirements.
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