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# AI Data Engineer

- **Company:** United IT Solutions
- **Location:** United States (Remote available)
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Engineering, Continuous Integration, Data Validation, Extract Transform Load (ETL), DevOps, Distributed Computing Environment, Distributed Systems, Github, Machine Learning, Open Source Technology, Performance Tuning, Systems Integration, Data Processing, Large Language Models, Apache Spark, Generative AI, Containerization, Pyspark, Kubernetes, Data Pipelines, Docker, Jenkins, Databricks
- **Published:** August 31, 2026
- **Apply:** https://www.dice.com/job-detail/4750957f-055a-4128-96d0-57bb43bb539c

## About the Role

· Experience: Proven experience as a Data Engineer building production-grade ETL/ELT data pipelines.

· LLM / AI Concepts: Minimum working knowledge of ML concepts, LLM architectures, vector databases, and RAG frameworks (e.g., LangChain, LlamaIndex).

· API Integration: Hands-on experience integrating third-party or self-hosted LLM APIs into data pipelines.

· Distributed Computing: Experience optimizing distributed data processing workloads (e.g., PySpark, Spark, Databricks, Ray, or Cloud-native processing services).

· CI/CD & Automation: Solid understanding of CI/CD pipeline implementation, deployment SDKs, and containerization (e.g., Docker, Kubernetes, GitHub Actions, Jenkins).

· Schema & Data Quality: Expertise in enforcing schema validation rules, data contracts, and pipeline performance optimization.

· Evaluation Metrics: Familiarity with AI/RAG evaluation metrics (e.g., retrieval precision, answer correctness, context relevance, faithfulness).

## Description

· Pipeline Engineering: Design, build, and maintain production-level data pipelines to deploy and operationalize ML and LLM workflows.

· LLM & RAG Integration: Implement Retrieval-Augmented Generation (RAG) frameworks using libraries like LangChain or LlamaIndex to query structured and unstructured data sources.

· API & System Integration: Integrate LLM APIs (e.g., OpenAI, Anthropic, or open-source models) into data processing workflows.

· Performance Optimization: Optimize distributed workloads, data processing engines, and pipeline latency for real-time and batch execution.

· CI/CD & DevOps: Build and maintain CI/CD pipelines to deploy data and AI workflows using relevant SDKs and automation tools.

· Data Quality & Validation: Implement strict schema validation rules and data quality checks to ensure reliable pipeline execution.

· AI Evaluation & Quality Control: Monitor and measure output quality using key metrics such as retrieval quality, answer correctness, and faithfulness.


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