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Staff Applied AI Engineer (.NET / Azure focus)
The Judge Group
40 minutes ago
Full-time
On-site
Chicago, Illinois, United States
** To ensure seamless team collaboration and operational impact, candidates must (currently) reside within commuting distance of the Chicago Loop/Downtown Area and adhere to a hybrid schedule (2–3 days/week on-site). Unrestricted U.S. work authorization is required. Please submit an application only if you fully meet these location and work authorization criteria. **
We are looking for a
Staff Applied AI Engineer
who has personally built, deployed, and scaled Generative AI features in production for real users.
In this role, you won’t just be designing systems in a vacuum—you will drop into product teams, teach engineers what GenAI can (and cannot) reliably do, and work side-by-side with them to ship high-impact features.
We value battle-tested, hands-on experience: seeking someone deeply grounded in the Microsoft stack (.NET, C#, Azure AI Services) who has stuck around long enough to see their GenAI systems break in production, debug complex failure modes, and build resilient, measurable platform primitives.
Key Responsibilities Embedded Technical Leadership:
Embed directly into product teams as the resident GenAI expert—teaching .NET/C# engineers how to build with modern AI architectures (Azure OpenAI, Semantic Kernel, LangChain.NET) and guiding initial production releases. Architecture & Full-Stack Execution:
Stay hands-on in full-stack C#/.NET development (web APIs, microservices, cloud-native backend architecture) while driving the end-to-end design of platform AI capabilities. Measurability & Production Reliability:
Design scalable evaluation (evals), telemetry (Azure Monitor, Application Insights), and automated feedback loops to ensure AI outputs are dependable, cost-effective, and safe at scale. Product Collaboration & Pragmatic Guidance:
Partner with product and solution leads to identify high-value customer problems—and have the pragmatic judgment to talk teams
out
of using GenAI when traditional software or classical algorithms are the better fit. Platform Mentorship & Tooling:
Enable core engineering teams to own their AI deliverables over time, establishing Azure-native platform primitives, CI/CD pipelines, and reusable developer tooling.
Requirements & Experience Production GenAI Experience:
7+ years of total software engineering experience, with a proven track record of personally shipping Generative AI / LLM applications into enterprise production environments. Core MS Tech Stack:
Strong, active command of
.NET / C#
development alongside deep cloud-native
Azure
experience (Azure OpenAI Service, Azure AI Search, Azure App Services, Functions, Container Apps, Cosmos DB, Azure DevOps). Battle-Tested Reliability:
Direct experience handling real-world GenAI failure modes (latency, hallucinations, context management, token/cost optimization, fallback mechanisms, and automated evaluation frameworks). Pragmatic Tech Leadership:
Demonstrated experience mentoring, influencing, and guiding non-AI engineering teams on best practices without requiring direct reporting lines.
Ideal Background & Relevant Profiles We screen on
what you’ve built, not who you built it for
. We actively look for candidates coming from backgrounds such as: Microsoft Global Black Belts (AI / Azure)
& Azure AI Customer Engineers Forward Deployed Engineers / Software Engineers
with strong C#/.NET backgrounds Enterprise AI Practice Consultants & Tech Leads Lead Applied AI Engineers
at enterprise software startups built natively on the Microsoft ecosystem.
Staff Applied AI Engineer
who has personally built, deployed, and scaled Generative AI features in production for real users.
In this role, you won’t just be designing systems in a vacuum—you will drop into product teams, teach engineers what GenAI can (and cannot) reliably do, and work side-by-side with them to ship high-impact features.
We value battle-tested, hands-on experience: seeking someone deeply grounded in the Microsoft stack (.NET, C#, Azure AI Services) who has stuck around long enough to see their GenAI systems break in production, debug complex failure modes, and build resilient, measurable platform primitives.
Key Responsibilities Embedded Technical Leadership:
Embed directly into product teams as the resident GenAI expert—teaching .NET/C# engineers how to build with modern AI architectures (Azure OpenAI, Semantic Kernel, LangChain.NET) and guiding initial production releases. Architecture & Full-Stack Execution:
Stay hands-on in full-stack C#/.NET development (web APIs, microservices, cloud-native backend architecture) while driving the end-to-end design of platform AI capabilities. Measurability & Production Reliability:
Design scalable evaluation (evals), telemetry (Azure Monitor, Application Insights), and automated feedback loops to ensure AI outputs are dependable, cost-effective, and safe at scale. Product Collaboration & Pragmatic Guidance:
Partner with product and solution leads to identify high-value customer problems—and have the pragmatic judgment to talk teams
out
of using GenAI when traditional software or classical algorithms are the better fit. Platform Mentorship & Tooling:
Enable core engineering teams to own their AI deliverables over time, establishing Azure-native platform primitives, CI/CD pipelines, and reusable developer tooling.
Requirements & Experience Production GenAI Experience:
7+ years of total software engineering experience, with a proven track record of personally shipping Generative AI / LLM applications into enterprise production environments. Core MS Tech Stack:
Strong, active command of
.NET / C#
development alongside deep cloud-native
Azure
experience (Azure OpenAI Service, Azure AI Search, Azure App Services, Functions, Container Apps, Cosmos DB, Azure DevOps). Battle-Tested Reliability:
Direct experience handling real-world GenAI failure modes (latency, hallucinations, context management, token/cost optimization, fallback mechanisms, and automated evaluation frameworks). Pragmatic Tech Leadership:
Demonstrated experience mentoring, influencing, and guiding non-AI engineering teams on best practices without requiring direct reporting lines.
Ideal Background & Relevant Profiles We screen on
what you’ve built, not who you built it for
. We actively look for candidates coming from backgrounds such as: Microsoft Global Black Belts (AI / Azure)
& Azure AI Customer Engineers Forward Deployed Engineers / Software Engineers
with strong C#/.NET backgrounds Enterprise AI Practice Consultants & Tech Leads Lead Applied AI Engineers
at enterprise software startups built natively on the Microsoft ecosystem.