ACL Digital
Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Engineer with a contract length of "unknown" at a pay rate of "unknown." Key skills include Python, SQL, and experience with LLMs, Generative AI, and cloud-based AI infrastructure, particularly Azure.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
October 31, 2025
🕒 - Duration
Unknown
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Columbus, OH
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🧠 - Skills detailed
#Deployment #Python #Data Engineering #Data Framework #Databricks #MLflow #ML (Machine Learning) #SQL (Structured Query Language) #Spark (Apache Spark) #Cloud #AI (Artificial Intelligence) #Azure Databricks #Azure #Langchain #Delta Lake #Data Management #Data Architecture #Model Deployment #Databases
Role description
We are seeking a Data Engineer to drive the integration of Large Language Models (LLMs) and Generative AI systems into our data ecosystem.
Required:
• Proven experience as a Data Engineer or ML Engineer with hands-on expertise in LLM or Generative AI system integrations.
• Strong proficiency in Python, SQL, and distributed data frameworks (e.g., Spark, DataBricks).
• Practical understanding of RAG architectures, vector databases (e.g., Pinecone, Weaviate, Chroma, FAISS), and embedding pipelines.
• Familiarity with LangChain, LlamaIndex, Semantic Kernel, or equivalent frameworks.
• Familiarity with MLflow, MLOps, and CI/CD for model deployment.
• Knowledge of medallion data architecture and Delta Lake for AI-ready data management.
• Solid understanding of cloud-based AI infrastructure—preferably Azure AI Services, Azure DataBricks, and Azure OpenAI Service
We are seeking a Data Engineer to drive the integration of Large Language Models (LLMs) and Generative AI systems into our data ecosystem.
Required:
• Proven experience as a Data Engineer or ML Engineer with hands-on expertise in LLM or Generative AI system integrations.
• Strong proficiency in Python, SQL, and distributed data frameworks (e.g., Spark, DataBricks).
• Practical understanding of RAG architectures, vector databases (e.g., Pinecone, Weaviate, Chroma, FAISS), and embedding pipelines.
• Familiarity with LangChain, LlamaIndex, Semantic Kernel, or equivalent frameworks.
• Familiarity with MLflow, MLOps, and CI/CD for model deployment.
• Knowledge of medallion data architecture and Delta Lake for AI-ready data management.
• Solid understanding of cloud-based AI infrastructure—preferably Azure AI Services, Azure DataBricks, and Azure OpenAI Service
