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# AI/ML Data Engineer

- **Company:** IMCS Group
- **Location:** Tampa, FL, United States
- **Experience:** Expert
- **Contract:** Temporary contract
- **Skills:** Training Data, Artificial Intelligence, Cloud Computing, Cloud Engineering, Databases, Data Cleansing, Health Information Technology, Python (Programming Language), Machine Learning, Systems Development Life Cycle, Redis, Software Engineering, Scripting, Feature Engineering, Large Language Models, Snowflake, Apache Spark, Model Validation, Generative AI, AI Platforms, Star Schema, Data Management, Machine Learning Operations, Virtual Agents, Data Pipelines
- **Published:** September 10, 2026
- **Apply:** https://www.careerbuilder.com/job-details/technology-lead-bpmi-ibm-bpmi-ibm-all-tampa-fl--8aed46ed-9553-4bbd-9901-5ba95336bf86

## About the Role

AI; Agentic AI; Machine Learning; Machine Learning Algorithms & Evaluation; Apache Spark; Redis; Snowflake; Vector Database; Data Preparation; Feature Engineering; Python, Algorithms, Apache Spark, Artificial Intelligence (AI), Call Centers, Cloud Computing, Customer Support/Service, Data Management, Diversity Recruiting, Finance, Fortune 500 Customers, Health Information Technology, IBM Product Family, Machine Learning, Python Programming/Scripting Language, Redis, Scalable System Development, Snowflake Schema, Software Engineering, Technical Leadership, Training Data Sets

## Description

Seeking an AI/ML Data Engineer with strong hands-on experience in Python, Agentic AI, machine learning algorithms, model evaluation, Apache Spark, Snowflake, Redis, vector databases, data preparation, and feature engineering. The candidate will build scalable AI/ML data pipelines, prepare model-ready datasets, develop feature-engineering workflows, support machine learning model training and evaluation, and implement retrieval pipelines for Agentic AI and GenAI applications. Experience with LLMs, RAG, embeddings, MLOps, and cloud-native AI platforms is preferred.
Writing improvements
Positioned Agentic AI and vector databases as core requirements rather than generic AI exposure.
Connected Spark, Snowflake, and Redis to practical data-platform responsibilities.
Included model evaluation, feature engineering, and AI-ready data preparation to clearly differentiate the role from a traditional Data Engineer.

Custom Fields:
Name: Intake Call Requested
Value: true


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