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# Machine Learning Engineer

- **Company:** Irvine Technology Corporation
- **Location:** Irvine, CA, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $145,600.0 - $166,400.0
- **Contract:** Contract
- **Skills:** Automation of Tests, Microsoft Azure, Cloud Database, Continuous Integration, Data Intelligence, Python (Programming Language), Machine Learning, Performance Tuning, Tensorflow, SQL Databases, Unstructured Data, Azure Data Factory, Sql Optimization, Retrieval-Augmented Generation, Large Language Models, Snowflake, Llamaindex, Generative AI, Pyspark, Scikit Learn, Information Technology, Machine Learning Operations, Software Version Control, Databricks
- **Published:** October 31, 2026
- **Apply:** https://www.dice.com/job-detail/32d466ba-3f40-443c-8955-77f0af4b4dfc

## About the Role

* Bachelor's degree in Computer Information Systems or equivalent experience, with 7+ years in machine learning and data science, including 3+ years in ML architecture and 2+ years in MLOps.
 * Strong hands-on experience with Databricks, Snowflake, Snowpark, PySpark, Azure Data Factory, and cloud-based data pipelines, including performance tuning and SQL optimization.
 * Proficiency in Python, SQL, and machine learning frameworks such as Scikit-Learn, with experience developing supervised and unsupervised models.
 * Experience building LLM-based applications, RAG solutions, and NLP workflows using technologies such as LangChain or LlamaIndex.
 * Healthcare industry experience required, including familiarity with healthcare data, HIPAA regulations, and handling sensitive Protected Health Information (PHI).

## Description

* Design, build, and optimize scalable machine learning pipelines using Databricks, PySpark, MLflow, Snowflake, and Snowpark.
 * Develop and deploy Generative AI, Large Language Model (LLM), and Retrieval-Augmented Generation (RAG) solutions to enhance automation and data intelligence.
 * Implement MLOps best practices, including CI/CD pipelines, model versioning, automated testing, and production monitoring using Azure DevOps and MLflow.
 * Architect and optimize cloud-based data environments, integrating structured and unstructured data through Azure Data Factory, PySpark, and SnowSQL.
 * Collaborate with cross-functional teams to drive AI strategy, establish development standards, and mentor engineers on machine learning best practices.