Job description

Top 5 Responsibilities

Architect and implement the AI Gateway. Design and build an Azure-native LLM gateway in C# providing unified ingress across multiple model providers (Azure OpenAI, Azure AI Foundry models, and others) - covering intelligent routing, fallback/load balancing, rate limiting and token-quota enforcement, semantic caching, and centralized API key/secret management. Reference the LiteLLM feature set as the functional bar to hit.
Build the observability and governance layer. Implement request/response tracing, prompt/completion logging, token and cost metering, and latency dashboards - the Langfuse-equivalent half of the stack - using Azure Monitor, Application Insights, OpenTelemetry, and APIM's native LLM logging/token-metric policies (or a self-hosted Langfuse instance where warranted).
Deliver multiple Agentic AI PoC scenarios. Using C# and the Microsoft Agent Framework, build a portfolio of distinct agentic patterns (single-agent tool use, multi-agent orchestration, human-in-the-loop workflows, RAG-grounded agents) mapped to real client business use cases - not one deep PoC, but several breadth-covering scenarios that demonstrate different capabilities.
Own technical delivery on client engagements end-to-end. Run architecture proposals, hands-on build, live demos, and production-readiness assessments directly with client technical stakeholders; translate ambiguous business asks into scoped, demoable agentic scenarios.
Package the work as reusable engineering assets. Turn the gateway and agent scenarios into templates, SDKs, or IaaC that can be re-deployed across environments rather than rebuilt from scratch each time - even though this is a client-facing role, the artifacts should outlive any single client environment.

Top 5 Required Skills / Background

Deep C#/.NET engineering background (12-15+ years). Production-grade API/service development, async patterns, dependency injection, testing discipline, and comfort operating without a large surrounding platform team.
Hands-on Azure platform expertise. Azure API Management (including GenAI/LLM-specific policies: token-limit, token-metric, semantic-caching), Azure OpenAI / Azure AI Foundry, Azure Monitor / Application Insights, Key Vault, and either Azure Functions/App Service or AKS for hosting gateway services.
Working knowledge of LLM gateway and observability platforms. Familiarity with LiteLLM, Langfuse, or comparable tools (Portkey, Kong AI Gateway) - not to operate them directly, but to translate their proven patterns (routing, fallback, semantic caching, token metering, tracing) into an Azure/C# implementation.
Microsoft Agent Framework experience, or a direct path into it. Hands-on work with MAF, or strong prior experience in Semantic Kernel and/or AutoGen (MAF's predecessors) - multi-agent orchestration, tool/function calling, thread/state management, and ideally MCP/A2A protocol exposure.
Client-facing solution engineering track record. Demonstrated ability to architect, prototype, and present technical PoCs directly to enterprise client stakeholders, defend design trade-offs live, and adapt scope under engagement time pressure.

Top 5 Required Skills / Background

Deep C#/.NET engineering background (12-15+ years). Production-grade API/service development, async patterns, dependency injection, testing discipline, and comfort operating without a large surrounding platform team.
Hands-on Azure platform expertise. Azure API Management (including GenAI/LLM-specific policies: token-limit, token-metric, semantic-caching), Azure OpenAI / Azure AI Foundry, Azure Monitor / Application Insights, Key Vault, and either Azure Functions/App Service or AKS for hosting gateway services.
Working knowledge of LLM gateway and observability platforms. Familiarity with LiteLLM, Langfuse, or comparable tools (Portkey, Kong AI Gateway) - not to operate them directly, but to translate their proven patterns (routing, fallback, semantic caching, token metering, tracing) into an Azure/C# implementation.
Microsoft Agent Framework experience, or a direct path into it. Hands-on work with MAF, or strong prior experience in Semantic Kernel and/or AutoGen (MAF's predecessors) - multi-agent orchestration, tool/function calling, thread/state management, and ideally MCP/A2A protocol exposure.
Client-facing solution engineering track record. Demonstrated ability to architect, prototype, and present technical PoCs directly to enterprise client stakeholders, defend design trade-offs live, and adapt scope under engagement time pressure.

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Alerts as often as hourly. Reach new roles while the pile is still small.

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Verified jobs, only. Say no to ghost jobs. Your time deserves respect.

Someone shared one job with you. ASAI keeps finding the rest.

Scored, not searched. Every role ranked against your actual profile.

Alerts as often as hourly. Reach new roles while the pile is still small.

Skill gaps, spelled out. See exactly which requirements you don't meet yet.

Verified jobs, only. Say no to ghost jobs. Your time deserves respect.

Job description

Top 5 Responsibilities

Architect and implement the AI Gateway. Design and build an Azure-native LLM gateway in C# providing unified ingress across multiple model providers (Azure OpenAI, Azure AI Foundry models, and others) - covering intelligent routing, fallback/load balancing, rate limiting and token-quota enforcement, semantic caching, and centralized API key/secret management. Reference the LiteLLM feature set as the functional bar to hit.
Build the observability and governance layer. Implement request/response tracing, prompt/completion logging, token and cost metering, and latency dashboards - the Langfuse-equivalent half of the stack - using Azure Monitor, Application Insights, OpenTelemetry, and APIM's native LLM logging/token-metric policies (or a self-hosted Langfuse instance where warranted).
Deliver multiple Agentic AI PoC scenarios. Using C# and the Microsoft Agent Framework, build a portfolio of distinct agentic patterns (single-agent tool use, multi-agent orchestration, human-in-the-loop workflows, RAG-grounded agents) mapped to real client business use cases - not one deep PoC, but several breadth-covering scenarios that demonstrate different capabilities.
Own technical delivery on client engagements end-to-end. Run architecture proposals, hands-on build, live demos, and production-readiness assessments directly with client technical stakeholders; translate ambiguous business asks into scoped, demoable agentic scenarios.
Package the work as reusable engineering assets. Turn the gateway and agent scenarios into templates, SDKs, or IaaC that can be re-deployed across environments rather than rebuilt from scratch each time - even though this is a client-facing role, the artifacts should outlive any single client environment.

Top 5 Required Skills / Background

Deep C#/.NET engineering background (12-15+ years). Production-grade API/service development, async patterns, dependency injection, testing discipline, and comfort operating without a large surrounding platform team.
Hands-on Azure platform expertise. Azure API Management (including GenAI/LLM-specific policies: token-limit, token-metric, semantic-caching), Azure OpenAI / Azure AI Foundry, Azure Monitor / Application Insights, Key Vault, and either Azure Functions/App Service or AKS for hosting gateway services.
Working knowledge of LLM gateway and observability platforms. Familiarity with LiteLLM, Langfuse, or comparable tools (Portkey, Kong AI Gateway) - not to operate them directly, but to translate their proven patterns (routing, fallback, semantic caching, token metering, tracing) into an Azure/C# implementation.
Microsoft Agent Framework experience, or a direct path into it. Hands-on work with MAF, or strong prior experience in Semantic Kernel and/or AutoGen (MAF's predecessors) - multi-agent orchestration, tool/function calling, thread/state management, and ideally MCP/A2A protocol exposure.
Client-facing solution engineering track record. Demonstrated ability to architect, prototype, and present technical PoCs directly to enterprise client stakeholders, defend design trade-offs live, and adapt scope under engagement time pressure.

Top 5 Required Skills / Background

Deep C#/.NET engineering background (12-15+ years). Production-grade API/service development, async patterns, dependency injection, testing discipline, and comfort operating without a large surrounding platform team.
Hands-on Azure platform expertise. Azure API Management (including GenAI/LLM-specific policies: token-limit, token-metric, semantic-caching), Azure OpenAI / Azure AI Foundry, Azure Monitor / Application Insights, Key Vault, and either Azure Functions/App Service or AKS for hosting gateway services.
Working knowledge of LLM gateway and observability platforms. Familiarity with LiteLLM, Langfuse, or comparable tools (Portkey, Kong AI Gateway) - not to operate them directly, but to translate their proven patterns (routing, fallback, semantic caching, token metering, tracing) into an Azure/C# implementation.
Microsoft Agent Framework experience, or a direct path into it. Hands-on work with MAF, or strong prior experience in Semantic Kernel and/or AutoGen (MAF's predecessors) - multi-agent orchestration, tool/function calling, thread/state management, and ideally MCP/A2A protocol exposure.
Client-facing solution engineering track record. Demonstrated ability to architect, prototype, and present technical PoCs directly to enterprise client stakeholders, defend design trade-offs live, and adapt scope under engagement time pressure.