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# Machine Learning Engineer (Data Engineering Focus)

- **Company:** Euclid Innovations
- **Location:** Charlotte, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Microsoft Azure, Continuous Integration, Information Engineering, Python (Programming Language), Machine Learning, Operational Databases, Standard Sql, Azure Machine Learning, Software Deployment, Google Cloud, Enterprise Software Applications, Feature Engineering, Data Ingestion, Delivery Pipeline, Large Language Models, Apache Spark, Generative AI, Git, Pyspark, Machine Learning Operations, Restful APIs, Data Pipelines, Databricks, Microservices
- **Published:** June 25, 2026
- **Apply:** https://www.dice.com/job-detail/8db3bf6c-94a3-4c86-9da6-a410b531bcac

## About the Role

* 8+ years of software/data engineering experience
 * Strong Python programming
 * Spark / PySpark
 * Databricks
 * SQL
 * Production Data Engineering
 * Data Ingestion Pipelines
 * Feature Engineering
 * Feature Pipeline Development
 * ML Inference Pipelines
 * Model Deployment Support
 * REST APIs / Microservices
 * CI/CD for ML Pipelines
 * Google Cloud Platform OR Azure or AWS Cloud
 * Git, * Experience supporting enterprise ML platforms.
 * Experience with MLOps tools.
 * Financial Services or Banking experience preferred.

Candidates with recent experience primarily focused on Data Science, LLM/Generative AI, or end-to-end model development are not the target profile. The ideal candidate should have hands-on experience in production ML engineering with a strong focus on data ingestion, feature engineering, inference pipelines, and production deployment.

## Description

We are seeking a Machine Learning Engineer with a strong Data Engineering background to support enterprise-scale machine learning platforms. The ideal candidate will partner closely with Data Scientists to build and optimize production-ready ML data pipelines, feature engineering frameworks, and inference infrastructure., * Build and maintain production data ingestion pipelines for machine learning.

 * Design and optimize feature engineering pipelines for large-scale ML workloads.
 * Partner with Data Scientists to prepare production-ready datasets and reusable features.
 * Support inference pipelines and production deployment.
 * Integrate ML services into enterprise applications.
 * Optimize feature pipelines for performance and scalability.
 * Monitor production KPIs, data quality, and pipeline health.
 * Maintain reproducible ML engineering workflows and version-controlled pipelines.


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