We are looking for a seasoned AI Architect with deep roots in the Microsoft/.NET ecosystem, strong cloud experience on Azure or Google Cloud Platform, and hands-on proficiency with AI-assisted development tooling as part of a modern, accelerated SDLC. You will design and scale AI-native systems while embedding intelligent developers tooling into every phase of the software delivery lifecycle.
Architect end-to-end AI solutions leveraging Azure AI Services or Google Cloud Platform Vertex AI / Gemini API
Champion and operationalize AI coding tools across the engineering organization to accelerate development, code review, testing, and documentation workflows
Define AI-assisted SDLC patterns: code generation guardrails, prompt standards, review workflows, and quality gates
Design and implement LLM-powered applications including RAG pipelines, AI agents, and multi-model orchestration workflows
Lead technical design for AI integrations into existing .NET / C# applications and microservices
Define patterns for prompt engineering, context management, embedding pipelines, and vector search
Evaluate and select AI tooling including orchestration frameworks (Semantic Kernel, LangChain.NET), vector databases, and observability platforms
Establish AI development standards, guardrails, cost governance models, and evaluation frameworks
Drive responsible AI practices: safety, bias mitigation, and model lifecycle management
Partner with product, data, and platform engineering teams to align AI capabilities with business outcomes
Mentor engineers on AI-native development practices and effective use of AI coding tools in day-to-day workflows
10+ years of software engineering experience with 3+ years in AI/ML architecture
Expert-level proficiency in C# and the .NET ecosystem (.NET 6/7/8, ASP.NET Core, Azure Functions)
Hands-on production experience on at least one of the following:
Azure: Azure OpenAI Service, Azure AI Studio, Azure Cognitive Services, AKS, API Management
Google Cloud Platform: Vertex AI, Gemini API, Google Cloud AI Services, GKE, Cloud Run, Apigee
Demonstrated experience with AI-assisted development tooling in a professional SDLC context, using one or more of the following:
Claude Code, GitHub Copilot, Cursor, Amazon Kiro, Tabnine, or equivalent
AI tooling for code generation, refactoring, debugging, test authoring, and documentation
Integrating AI coding tools into CI/CD pipelines and team developer workflows
Establishing team-level standards and best practices for AI-assisted development
Proven track record building production LLM applications: RAG, agents, tool use, function calling
Strong command of Semantic Kernel or comparable .NET-native AI orchestration frameworks
Experience with vector databases such as Azure AI Search, Vertex AI Vector Search, Pinecone, Weaviate, or Qdrant
Solid grounding in cloud-native architecture: containerization, API gateway patterns, event-driven design, and managed services
Experience integrating AI coding tools with GitHub Actions, Azure DevOps, or Cloud Build pipelines
Exposure to Python-based ML tooling (LangChain, LlamaIndex, Hugging Face) with ability to bridge into .NET environments
Experience with MLOps practices: model versioning, CI/CD for AI, and drift monitoring via Azure ML or Vertex AI Pipelines
Familiarity with multi-agent frameworks and agentic workflow design
Experience measuring SDLC productivity improvements from AI tooling adoption: cycle time, defect rate, PR throughput
Prior experience in regulated industries (finance, healthcare, government) a strong plus
Relevant certifications (any of the following):
Azure Solutions Architect Expert
Azure AI Engineer Associate
Google Cloud Platform Professional Cloud Architect
Google Cloud Platform Professional ML Engineer
Never miss an opportunity! Create an alert based on the job you applied for.