AI Engineer
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Job Description
AI Engineer building C#/.NET and Azure-based enterprise AI agents for AnywhereNow’s dialogue-management platforms. Developing secure MCP integrations and production-ready intelligent systems.
Responsibilities:
- Design, build, and deploy AI agents that solve customer and business problems
- Translate product requirements and use cases into scalable, production-ready solutions
- Build and operate MCP servers providing secure access to tools, systems, and enterprise data
- Define tool interfaces, authentication mechanisms, and resource access patterns
- Integrate agents with Microsoft Dynamics 365, Microsoft Graph, Dataverse, ServiceNow, contact centre/customer experience platforms, and internal or third-party APIs
- Define memory, context, decision-making, retrieval, reasoning, and tool-use strategies
- Design guardrails for safe and effective enterprise agent operation
- Develop adapters, event-processing capabilities, and integration layers
- Enable agents to share insights across an interconnected intelligence ecosystem
- Collaborate with Product, Engineering, QA, and AI leadership
- Contribute technical expertise, customer insights, and market observations to the Agent Factory roadmap and backlog
- Support information security by following policies, protecting data, securely using systems, reporting incidents, completing required training, and handling confidential information securely
Requirements:
- Strong software engineering experience using C# and .NET
- Hands-on experience with Azure services, including Azure Functions, Azure App Service, Azure OpenAI Service, and Azure AI Foundry / AI Studio
- Experience integrating with Dynamics 365, Dataverse, Microsoft Graph, or Power Platform
- Knowledge of Azure security and identity services, including Microsoft Entra ID, Managed Identities, and Azure Key Vault
- Practical experience building or consuming MCP servers
- Knowledge of Semantic Kernel, AutoGen, or Microsoft AI SDKs
- Experience with function and tool calling, RAG, vector databases, and context and memory management
- Strong API integration experience, including REST APIs, webhooks, and event-driven architectures
- Experience with cloud-native deployment, CI/CD pipelines, and infrastructure-as-code approaches
- Understanding of performance, latency, reliability, and cost considerations for production LLMs
- Commitment to software quality, testing, and continuous improvement
- Ability to work closely with QA teams on agent evaluation, testing, and quality assurance
- Experience with multi-agent architectures, regulated environments, LLM observability, contact centre/customer experience platforms, or enterprise workflow automation is nice to have
Benefits:
- No specific benefits, perks, or compensation extras are stated in the posting
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