11 of 11 Permanent Semantic Kernel Jobs in the North of England

frameworks.Build and deploy agentic AI solutions to automate workflows and enhance business processes.Develop Retrieval Augmented Generation (RAG) solutions using vector databases and semantic search technologies.Design and implement AI orchestration pipelines using frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen or similar.Integrate AI solutions with … Models and AI services such as OpenAI, Azure OpenAI, Anthropic Claude or equivalent.Hands-on experience with AI orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI or similar.Experience developing and deploying cloud-native applications within Azure, AWS or GCP.Strong understanding of APIs, system integration ...

experience with LLM integration, RAG architectures, AI orchestration frameworks, and Agentic AI solutions. Experience with AI frameworks and tools such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or equivalent. #J-18808-Ljbffr ...

Experience with LLM integration, RAG, AI orchestration, or Agentic AI frameworks. Exposure to AI platforms such as Azure OpenAI, OpenAI, LangChain, LangGraph, or Semantic Kernel. Candidate Profile Strong engineering and problem-solving skills. Product engineering mindset with a focus on quality and scalability. Comfortable working within existing technical ...

both technical and non-technical stakeholders.Desirable Experience designing and implementing AI agents, copilots and intelligent automation solutions.Knowledge of Azure Functions, Azure AI Search, Semantic Kernel, Microsoft Agent Framework or similar technologies.Experience with data modelling, governance, security and enterprise architecture.Exposure to machine learning, predictive analytics or forecasting ...

Language: Python preferable Frameworks: Agent Development Kits (ADKs) ; e.g. Google ADK, LangGraph, CrewAI, OpenAI Agents SDK, AWS Bedrock AgentCore, Microsoft Agent Framework/Semantic Kernel. Framework choice follows the engagement; the discipline is the same. Models: Multi-LLM via the kit (e.g. Claude on Bedrock, Gemini, Azure OpenAI … building with at least one agent development kit (Google ADK, LangGraph, CrewAI, OpenAI Agents SDK, AWS Bedrock AgentCore or Microsoft Agent Framework/Semantic Kernel). Solid command of agent engineering: prompt engineering, context engineering, prompt caching, RAG/context graphs, tool/function calling ...

deploying production applications within Microsoft Azure. Exposure to building AI-enabled applications using technologies such as Azure AI, Microsoft AI Foundry, Azure OpenAI, Semantic Kernel, Agent Framework or similar. Understanding of RAG, LLMs, vector search and agentic AI concepts. Experience integrating applications with APIs, cloud services ...

automated environment provisioning. AI Tooling & MCP: Hands-on experience or working knowledge of Model Context Protocol (MCP) architecture, agent orchestration frameworks (e.g., Semantic Kernel, AutoGen, LangChain), and API gateway patterns. Containerization: Experience deploying containerized microservices using Azure Container Apps (ACA) or Azure Kubernetes Service (AKS). ...

with AI, machine learning or generative AI technologiesExperience working with Azure and cloud-based technologiesKnowledge of technologies such as Azure OpenAI, Microsoft Foundry, Semantic Kernel, RAG or vector databasesUnderstanding of AI agents, LLMs and modern AI development approachesStrong software engineering fundamentals, including source control, testing ...

automation Python, C# and/or TypeScript APIs and system integration SQL/databases Cloud platforms, particularly Azure Frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen or similar Most importantly, you will be someone who understands how to turn AI capabilities into robust, commercially useful software. ...

functionality directly into software products Working with technologies such as OpenAI, Azure OpenAI, Claude, Gemini or local models Exploring RAG, AI agents, Semantic Kernel, MCP, vector search and intelligent workflows Engineering around the realities of production AI: accuracy, security, validation, guardrails, latency and cost Working across ...

functionality directly into software products Working with technologies such as OpenAI, Azure OpenAI, Claude, Gemini or local models Exploring RAG, AI agents, Semantic Kernel, MCP, vector search and intelligent workflows Engineering around the realities of production AI: accuracy, security, validation, guardrails, latency and cost Working across ...