AI/ML Data Platform Engineer

Hello,

I Hope you are doing well!

This is Raghvendra Singh from Creative Global Consultancy.

I have a Job opportunity, Please find the details in the below mentioned JD. If you are interested, Please revert me back with your updated resume copy.

Job Details:

AI/ML Data Platform Engineer



Location: Linthicum Heights, MD



Work Arrangement: Onsite



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 GCP 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.