Senior Azure Generative AI Engineer
$95–$115,000 year
On-siteDallas, Texas, United States
Job Summary
Design, develop, and deploy enterprise-grade Generative AI solutions using Microsoft Azure OpenAI Service, Azure AI Foundry, and Azure Machine Learning. Build scalable applications, implement Retrieval Augmented Generation (RAG) architectures, and develop data ingestion pipelines to support model training and inference. Fine-tune Large Language Models, integrate services via REST APIs, and enforce MLOps best practices for governance and lifecycle management. Collaborate with stakeholders to define solution strategies while ensuring compliance with banking and financial security regulations. Troubleshoot production issues to optimize model accuracy, performance, and cost. This role requires 6+ years of engineering experience and 3+ years on Azure. Salary ranges from $95-115K.
Required Qualifications
- Minimum 6+ years of software engineering, data engineering, or AI/ML development experience
- Minimum 3+ years of hands-on experience developing solutions on Microsoft Azure
- Minimum 2+ years of hands-on experience building Generative AI or Large Language Model (LLM) applications
- Strong experience with Azure OpenAI Service
- Hands-on experience with Azure AI Foundry (formerly Azure AI Studio)
- Experience with Azure Machine Learning and Azure Cognitive Services
- Strong understanding of Retrieval Augmented Generation (RAG) architecture
- Experience with vector databases such as Azure AI Search, Pinecone, Weaviate, or similar technologies
- Strong Python programming skills
- Experience with LangChain, LangGraph, Semantic Kernel, or similar GenAI orchestration frameworks
- Experience with PyTorch, Hugging Face Transformers, or similar ML frameworks
- Experience building scalable data pipelines using Azure Data Factory, Synapse, Microsoft Fabric, Databricks, or Spark
- Understanding of prompt engineering, model evaluation, fine-tuning, embeddings, and LLM optimization
- Experience with CI/CD pipelines and MLOps/LLMOps practices
- Strong understanding of AI governance, responsible AI, and model security
Desired Qualifications
- Experience delivering enterprise cloud solutions within regulated industries such as banking or financial services is highly preferred
- Familiarity with Git, Azure DevOps, and container technologies (Docker/Kubernetes) is preferred
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