Lead AI Engineer – AI Agents (Semantic Kernel)
Must Have Technical/Functional Skills
We’re seeking a hands-on technical leader to architect, build, and operate AI Agents powered by Microsoft Semantic Kernel. You’ll own end-to-end delivery: from prototyping and containerizing AI workloads, to setting up observability, to deploying and scaling in Azure. A .NET background is preferred. Semantic Kernel is mandatory skill.
• 7+ years of software engineering experience, with 3+ years leading high-visibility projects
• Deep expertise in Semantic Kernel for .NET 8+
• Hands-on experience with Azure compute and AI services:
o AKS, ACI, App Service, Azure Functions
o Azure OpenAI Service, Cognitive Search, Cosmos DB or Azure SQL
• Strong containerization skills: Docker, multi-stage builds, image security scanning
• CI/CD pipelines experience (Azure DevOps, GitHub Actions) for automated builds and deployments
• Observability and monitoring proficiency:
o Instrumentation with Application Insights / OpenTelemetry
o Log Analytics, Prometheus, Grafana dashboards
• Solid background in RESTful API design, microservices, and event-driven architectures
• Familiarity with security and compliance in cloud environments (Azure Policy, Key Vault, RBAC)
• Excellent communication skills; proven ability to drive technical decisions and mentor teams
Roles & Responsibilities
- Lead design and implementation of AI Agents using Semantic Kernel, orchestrating prompts, skills, memory, and plugins
- Containerize services into Docker images; author and maintain Dockerfiles and multi-stage CI/CD pipelines
- Deploy and manage containerized workloads on Azure
- Integrate with Azure AI services (OpenAI, Cognitive Services) for embedding, completions, and vector stores
- Instrument services for observability on Azure
- Define and enforce best practices for secure configuration, secret management (Azure Key Vault), and identity (Managed Identities)
- Collaborate with data engineers, ML engineers, and architects to integrate retrieval-augmented generation (RAG) and knowledge bases
Nice to have:
• Experience with vector and/or graph databases
• Prior work on retrieval-augmented generation or custom embedding pipelines
• Knowledge of Terraform for infrastructure as code
• Open source contributions or published Semantic Kernel examples
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