Job description

Roles and Responsibilities Lead the design and development of AI Agents using Microsoft Semantic Kernel, orchestrating prompts, skills, memory, and plugins Own containerization and deployment of services using Docker, including authoring secure, efficient multi-stage builds Build and maintain CI/CD pipelines (Azure DevOps, GitHub Actions) for automated testing and deployment Operate containerized workloads on Azure, leveraging AKS, ACI, Azure Functions, and App Services Integrate Azure AI services such as Azure OpenAI, Cognitive Search, Cosmos DB, and Azure SQL Implement observability using Application Insights, OpenTelemetry, Prometheus, and Grafana Ensure secure operations via Azure Key Vault, Managed Identities, and RBAC Collaborate with engineering teams to support RAG (retrieval-augmented generation) and integrate custom knowledge bases Drive technical best practices, mentoring teams on scalable architectures and cloud-native design patterns Required Skills & Qualifications 7+ years of software engineering experience, including 3+ years leading complex, high-visibility projects Deep hands-on experience with Microsoft Semantic Kernel (for .NET 8+) Strong .NET engineering background Expertise in Azure services, including: AKS, ACI, Azure App Service, Azure Functions Azure OpenAI, Azure Cognitive Search, Cosmos DB, or Azure SQL Advanced Docker and containerization skills, including image optimization and security scanning Proficient in building CI/CD pipelines with Azure DevOps or GitHub Actions Demonstrated ability to instrument applications for observability with Application Insights, OpenTelemetry, and visual dashboards (e.g., Grafana) Strong background in RESTful APIs, microservices, and event-driven architecture Solid understanding of cloud security principles and governance using Azure Policy, Key Vault, and RBAC Excellent verbal and written communication skills with a track record of mentoring and leading teams Preferred Experience Experience with vector and/or graph databases Background in retrieval-augmented generation (RAG) or custom embedding pipelines Working knowledge of Terraform for infrastructure as code Contributions to open-source projects, particularly in the Semantic Kernel ecosystem

Requirements