AI/ML Data Platform Engineer
Triwave Solutions
Linthicum Heights, MD, United States
6 days ago
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Role details
Contract type
Contract
Employment type
Full-time (> 32 hours)
Experience level
Experienced
Experience required
2 years minimum
Working hours
Regular working hours
Job source
Tech stack
Application Programming Interfaces (APIs)
Artificial Intelligence
Airflow
Amazon Web Services
Data Analysis
Apache HTTP Server
Microsoft Azure
BigQuery
Data as a Services
Information Engineering
Data Infrastructure
Data Integration
+30 more
Extract Transform Load (ETL)
Data Vault Modeling
Python (Programming Language)
Machine Learning
Tensorflow
Service Design
Software Engineering
SQL Databases
Data Streaming
Systems Integration
Feature Store
Data Processing
Google Cloud
Feature Engineering
Pytorch
Great Expectations (Foster Youth College-readiness and Support Program)
Snowflake
Model Validation
Event Driven Architecture
Scikit Learn
Information Technology
Collibra
Star Schema
Apache Kafka
Data Management
Video Streaming
Restful APIs
Data Pipelines
Amazon Redshift
Databricks
Job description
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.
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.
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Prepare application
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