Senior Azure AI Foundry Engineer (GenAI, LLMs & RAG)

RESOURCESOFT, INC.; United States; On-site; USD 150,000 - 230,000; Full time

Job summary

Resourcesoft, Inc. seeks a senior cloud AI/ML engineer to design, build, and operationalize generative AI solutions on Azure, leveraging Azure AI Foundry and Azure OpenAI Service. This role demands hands-on development with Python or C#, and deep experience in LLM orchestration within enterprise environments.

The candidate will integrate AI capabilities across Azure services, implement RAG and vector retrieval pipelines, and ensure secure, scalable AI workloads across tenant environments.

Qualifications

• 6+ years of cloud AI/ML engineering experience.
• Senior-level expertise in Azure-based solution design.
• Experience with Azure AI Foundry for generative AI solutions.
• Strong Python or C# development skills.
• Experience with LLMs, prompt orchestration, and Azure OpenAI Service.
• Experience with RAG architectures, vector databases, and knowledge retrieval.

Responsibilities

• Design, build, and operationalize generative AI solutions using Azure AI Foundry.
• Configure and manage LLMs, prompt orchestration, and AI workflows.
• Integrate AI solutions with Azure services, APIs, and enterprise systems.
• Implement RAG architectures, vector databases, and knowledge retrieval pipelines.
• Establish security, monitoring, and governance controls for AI workloads.
• Optimize model performance, cost, and scalability, and deliver an overview/handoff once the build is complete.

Skills

Azure cloud AI/ML

Azure AI Foundry

LLMs

Prompt orchestration

Azure OpenAI Service

RAG architectures

Vector databases

Knowledge retrieval

Azure Functions

Logic Apps

API Management

Python

C#

Senior-level design

Job description

Resourcesoft, Inc. seeks a senior cloud AI/ML engineer to design, build, and operationalize generative AI solutions on Azure, leveraging Azure AI Foundry and Azure OpenAI Service. This role demands hands-on development with Python or C#, and deep experience in LLM orchestration within enterprise environments.

The candidate will integrate AI capabilities across Azure services, implement RAG and vector retrieval pipelines, and ensure secure, scalable AI workloads across tenant environments.