Position Summary/About the role
Title: AI Application Developer (.NET / Azure AI)
Location: Remote, MN
Duration: 6 months
Job Description:
Overview We are seeking an experienced AI Application Developer with strong .NET development expertise to support an enterprise technology division responsible for modernizing operational systems through artificial intelligence.
This role will focus on designing and implementing AI-powered applications that improve operational workflows, automation, and digital services. The developer will work on initiatives such as intelligent virtual assistants, internal operational AI tools, and service delivery enhancements.
The position requires hands-on development of modern AI-enabled web applications that integrate enterprise systems with large language models deployed in both cloud and local environments. The developer will build solutions using ASP.NET, Azure AI Services, vector search technologies, and modern AI orchestration frameworks to deliver scalable and secure applications.
The role operates in an agile development environment and requires collaboration with AI engineers, data scientists, DevOps engineers, and enterprise application teams.
Key Responsibilities AI Application Development Design and develop AI-enabled applications that support enterprise operational systems and digital services.
Responsibilities include:
- Develop and maintain ASP.NET Core web applications integrated with AI services
- Build AI-powered solutions such as virtual assistants, chat interfaces, and operational automation tools
- Develop APIs and services that connect enterprise systems with AI models
- Implement AI-driven features into existing enterprise web applications
- Design scalable and secure application architectures that support AI functionality
AI Model Integration and Architecture Design and implement AI model integration frameworks supporting both cloud and local inference environments.
Responsibilities include:
- Integrate Azure AI services such as Azure OpenAI, Cognitive Services, and Cognitive Search
- Deploy hybrid AI solutions using both cloud-hosted and locally hosted models
- Implement Retrieval-Augmented Generation (RAG) pipelines for contextual AI responses
- Design and implement vector search and semantic search capabilities
- Integrate vector databases and embedding pipelines for enterprise data retrieval
- Build systems that enable contextual knowledge retrieval from enterprise data sources
AI Agent and Workflow Development Develop advanced AI agent orchestration frameworks and intelligent workflows.
Responsibilities include:
- Implement intelligent multi-agent workflows using frameworks such as Semantic Kernel or AutoGen
- Develop agent orchestration systems capable of multi-step reasoning and task automation
- Implement Model Context Protocol (MCP) or similar frameworks to enable interoperability between agents and services
- Design agent-driven automation that enhances operational efficiency and digital services
Performance Optimization and AI Infrastructure Ensure AI solutions are scalable, efficient, and optimized for enterprise deployment.
Responsibilities include:
- Optimize model performance, latency, and operational costs
- Manage inference across hybrid environments including cloud and local LLM deployments
- Support scalable AI application deployment strategies
- Work with DevOps teams to integrate AI pipelines into CI/CD workflows
Agile Collaboration and Project Delivery Work within an agile development framework to deliver production-ready AI features.
Responsibilities include:
- Participate in agile development sprints with work typically assigned in three-week sprint cycles
- Collaborate with AI engineers, software developers, DevOps engineers, and data scientists
- Provide weekly progress updates and documentation of work performed
- Communicate regularly with technical leadership regarding development progress and blockers
- Deliver high-quality, production-ready code and technical documentation
Required Qualifications - Bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience
- 3 years of experience developing applications using ASP.NET Core or ASP.NET MVC
- Strong experience building applications using C# and RESTful APIs
- Experience integrating with Azure AI services such as Azure OpenAI, Cognitive Services, or Cognitive Search
- Experience running and integrating locally hosted large language models using tools such as Ollama
- Hands-on experience using AI orchestration frameworks such as Semantic Kernel or AutoGen
- Strong understanding of AI architecture patterns including Retrieval-Augmented Generation (RAG), vector search, and semantic search
- Experience working with asynchronous programming and scalable service architectures
- Familiarity with Azure Blob Storage, Azure Functions, and Azure DevOps CI/CD pipelines
Preferred Qualifications Candidates with the following experience will be highly valued:
- Experience building AI copilots or enterprise virtual assistants
- Development of LLM-powered chat interfaces or conversational AI applications
- Experience building multi-agent AI systems
- Knowledge of embedding generation and vector indexing techniques
- Experience implementing hybrid retrieval strategies for enterprise data
- Familiarity with Model Context Protocol (MCP) or similar AI communication protocols
- Exposure to frameworks such as LangChain or ML.NET
- Experience implementing OpenAI function calling or similar tool integration approaches
- Experience deploying AI models in both cloud-hosted and local inference environments
- Experience implementing AI solutions within enterprise or government environments
- Familiarity with accessibility standards such as WCAG
Technical Skills Summary Strong candidates will demonstrate experience in the following technologies:
- Programming
C#
ASP.NET Core
ASP.NET MVC
REST APIs - AI Frameworks
Semantic Kernel
AutoGen
LangChain
ML.NET - AI Architecture
Large Language Models
Retrieval Augmented Generation (RAG)
Vector Search
Semantic Search
AI Agents - Cloud Platforms
Microsoft Azure
Azure AI Services
Azure OpenAI
Azure Cognitive Search - AI Infrastructure
Ollama
Local LLM inference
Hybrid cloud AI architectures - DevOps
Azure DevOps
CI/CD pipelines
Cloud deployment automation
Work Environment - Agile development environment using sprint-based delivery cycles
- Cross-functional collaboration with AI engineers, DevOps teams, and enterprise application developers
- Weekly progress reporting and regular technical updates
- Enterprise technology environment focused on modernizing digital services through AI
The Role Is More AI Architect Than Basic Developer Although the role is labeled as an application developer, the candidate must understand:
- LLM architecture
- AI orchestration frameworks
- enterprise AI design patterns
This is closer to an
AI Solutions Engineer or AI Platform Developer.
Enterprise AI Integration Experience Is Critical Candidates must be comfortable integrating AI into existing enterprise systems rather than building standalone AI demos.
Look for experience with:
- enterprise APIs
- enterprise data pipelines
- enterprise application modernization
Hybrid AI Environments Are a Core Requirement Candidates must understand:
- cloud AI models
- local model deployment
- hybrid inference architectures
This is a
rare skill combination, so candidates with Ollama or local LLM deployment experience are extremely valuable.
Communication Skills Are Very Important The candidate will be expected to:
- provide weekly updates
- document development progress
- collaborate across technical teams
This role requires someone who can
communicate technical concepts clearly to leadership and cross-functional teams.
