AI Application Developer (.NET / Azure AI)

Descrição da Vaga

Requisitos

• 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
• 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
• Strong candidates will demonstrate experience in the following technologies:
• Programming
• NET Core
• NET MVC
• AI Frameworks
• DevOps
• Azure DevOps
• 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
• enterprise APIs
• local model deployment
• This is a rare skill combination, so candidates with Ollama or local LLM deployment experience are extremely valuable
• Communication Skills Are Very Important
• This role requires someone who can communicate technical concepts clearly to leadership and cross-functional teams

Responsabilidades

• 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
• Design and develop AI-enabled applications that support enterprise operational systems and digital services
• 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
• 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
• 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
• Ensure AI solutions are scalable, efficient, and optimized for enterprise deployment
• 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
• 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
• 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
• provide weekly updates
• document development progress
• collaborate across technical teams