26 to 39 of 39 LangChain Jobs in the South East

have: 1+ year of fullstack experience (open to senior/staff-level engineers too) Production experience with Model Context Protocol (MCP) is highly desired Experience working with LLMs, RAGs, Langchain, OpenAI, Claude etc is beneficial Generative AI Product experience Production-grade Python skills Frontend experience with Vue + TypeScript , or React and a willingness to learn Vue Understanding of web More ❯

or applied machine learning roles. Proven track record of delivering production-grade AI solutions in complex, data-rich environments. Proficiency in Python and key ML libraries (e.g. Semantic Kernel, Langchain and Agentic frameworks). Experience deploying models into production using APIs, containers, or cloud-native services. Familiarity with Azure and infrastructure-as-code tools. Strong knowledge of data engineering principles More ❯

Extensive hands-on experience with Large Language Models (such as GPT, Claude, LLaMA) and prompt optimisation techniques Proven track record of embedding LLMs into business processes using frameworks like LangChain, LlamaIndex, or similar technologies Advanced programming skills in Python or JavaScript for rapid prototyping and system integration Familiarity with workflow automation solutions (UiPath, Power Automate, Zapier, n8n) and API development More ❯

team-oriented, and have a strong ability to learn. Responsibilities Design, develop, and evaluate robust AI agent systems and workflows using large language models [LLMs] and modern frameworks [e.g., LangChain, LangGraph]. Own the full LLM application lifecycle: from prototyping and prompt engineering to deployment, monitoring, and maintenance in production. Architect and implement efficient Retrieval-Augmented Generation [RAG] systems utilizing … with a focus on AI 1+ years of hands-on experience building applications with Large Language Models [LLMs] Strong proficiency in Python and experience with modern LLM frameworks [e.g., LangChain, LangGraph, CrewAI] Proven experience with the full ML Ops lifecycle: building, testing, deploying, and monitoring models in production [using tools like Docker, Kubernetes, MLflow, Weights & Biases]. Solid understanding of More ❯

team-oriented, and have a strong ability to learn. Responsibilities Design, develop, and evaluate robust AI agent systems and workflows using large language models [LLMs] and modern frameworks [e.g., LangChain, LangGraph]. Own the full LLM application lifecycle: from prototyping and prompt engineering to deployment, monitoring, and maintenance in production. Architect and implement efficient Retrieval-Augmented Generation [RAG] systems utilizing … with a focus on AI 1+ years of hands-on experience building applications with Large Language Models [LLMs] Strong proficiency in Python and experience with modern LLM frameworks [e.g., LangChain, LangGraph, CrewAI] Proven experience with the full ML Ops lifecycle: building, testing, deploying, and monitoring models in production [using tools like Docker, Kubernetes, MLflow, Weights & Biases]. Solid understanding of More ❯

team-oriented, and have a strong ability to learn. Responsibilities Design, develop, and evaluate robust AI agent systems and workflows using large language models [LLMs] and modern frameworks [e.g., LangChain, LangGraph]. Own the full LLM application lifecycle: from prototyping and prompt engineering to deployment, monitoring, and maintenance in production. Architect and implement efficient Retrieval-Augmented Generation [RAG] systems utilizing … with a focus on AI 1+ years of hands-on experience building applications with Large Language Models [LLMs] Strong proficiency in Python and experience with modern LLM frameworks [e.g., LangChain, LangGraph, CrewAI] Proven experience with the full ML Ops lifecycle: building, testing, deploying, and monitoring models in production [using tools like Docker, Kubernetes, MLflow, Weights & Biases]. Solid understanding of More ❯

and taking ownership of technical projects. • Excellent analytical, problem-solving, and time management skills. Bonus skills • Experience deploying and managing applications with Azure and Docker. • Familiarity with frameworks like LangChain and Retrieval-Augmented Generation (RAG) models for AI-driven applications. • Experience with pandas for data manipulation. What’s on offer • Regular salary reviews recognising performance and contribution. • Generous annual leave More ❯

No-Code: Ability to leverage low-code and no-code solutions where appropriate to accelerate delivery. Nice to Have Exposure to agentic AI, including experience with LLMs, MCP servers, LangChain, and prompt engineering. To apply, please submit your CV and daily rate expectation by clicking on the relevant links. If successful you will be contacted by one of our consultants More ❯

No-Code: Ability to leverage low-code and no-code solutions where appropriate to accelerate delivery. Nice to Have Exposure to agentic AI, including experience with LLMs, MCP servers, LangChain, and prompt engineering. To apply, please submit your CV and daily rate expectation by clicking on the relevant links. If successful you will be contacted by one of our consultants More ❯

HR function. You will work closely with HR stakeholders, data scientists, and legal compliance teams to build secure and scalable multi-agent AI solutions using modern frameworks such as LangChain, AutoGen, LangGraph, and RAG. Key Responsibilities: Architect and implement AI-powered tools to automate and enhance HR workflows (e.g., onboarding, employee queries, policy navigation). Design and deploy conversational agents … using LangChain and AutoGen for internal use. Build modular and scalable pipelines using LangGraph for multi-agent orchestration. Integrate RAG systems to enable context-aware document retrieval from internal HR/legal databases. Required Skills & Experience: Solid exposure in AI/ML engineering, with a strong focus on NLP and LLMs. Proven hands-on knowledge with: LangChain, AutoGen, LangGraph and More ❯

practices, and observability tools to ensure reliable, high-performing systems. On the AI side, you have practical experience working with large language models (LLMs), prompt engineering, and frameworks like LangChain, with knowledge of advanced patterns such as Retrieval-Augmented Generation (RAG) and agent-based systems. A strong foundation in Agile SDLC practices is essential, along with familiarity in containerization, DevOps … in building microservices, SaaS solutions, and API-first architectures to enable scalable, interoperable systems. Frameworks & Tools: Backend: Spring Boot (web, data, cloud, security), JUnit 5, Mockito, database integration. AI: LangChain, Retrieval-Augmented Generation (RAG), MCP servers (as consumer and developer), and prompt engineering for LLM optimization. Exposure to popular libraries and frameworks (Apache Commons, Guava, Swagger, TestContainers). Architecture & Platforms … application security and model safety considerations. AI Development: End-to-end development of AI-powered solutions, from concept to production readiness. Practical experience with LLMs and agentic AI, including LangChain integration, RAG pipelines, prompt engineering, and model consumption via APIs. Ability to bridge backend and AI workflows, integrating advanced AI capabilities into scalable enterprise platforms. SDLC & Agile Practices: Familiar with More ❯

practices, and observability tools to ensure reliable, high-performing systems. On the AI side, you have practical experience working with large language models (LLMs), prompt engineering, and frameworks like LangChain, with knowledge of advanced patterns such as Retrieval-Augmented Generation (RAG) and agent-based systems. A strong foundation in Agile SDLC practices is essential, along with familiarity in containerization, DevOps … in building microservices, SaaS solutions, and API-first architectures to enable scalable, interoperable systems. Frameworks & Tools:Backend: Spring Boot (web, data, cloud, security), JUnit 5, Mockito, database integration. AI: LangChain, Retrieval-Augmented Generation (RAG), MCP servers (as consumer and developer), and prompt engineering for LLM optimization. Exposure to popular libraries and frameworks (Apache Commons, Guava, Swagger, TestContainers). Architecture & Platforms … application security and model safety considerations. AI Development: End-to-end development of AI-powered solutions, from concept to production readiness. Practical experience with LLMs and agentic AI, including LangChain integration, RAG pipelines, prompt engineering, and model consumption via APIs. Ability to bridge backend and AI workflows, integrating advanced AI capabilities into scalable enterprise platforms. SDLC & Agile Practices: Familiar with More ❯

practices, and observability tools to ensure reliable, high-performing systems. On the AI side, you have practical experience working with large language models (LLMs), prompt engineering, and frameworks like LangChain, with knowledge of advanced patterns such as Retrieval-Augmented Generation (RAG) and agent-based systems. A strong foundation in Agile SDLC practices is essential, along with familiarity in containerization, DevOps … in building microservices, SaaS solutions, and API-first architectures to enable scalable, interoperable systems. Frameworks & Tools: Backend: Spring Boot (web, data, cloud, security), JUnit 5, Mockito, database integration. AI: LangChain, Retrieval-Augmented Generation (RAG), MCP servers (as consumer and developer), and prompt engineering for LLM optimization. Exposure to popular libraries and frameworks (Apache Commons, Guava, Swagger, TestContainers). Architecture & Platforms … application security and model safety considerations. AI Development: End-to-end development of AI-powered solutions, from concept to production readiness. Practical experience with LLMs and agentic AI, including LangChain integration, RAG pipelines, prompt engineering, and model consumption via APIs. Ability to bridge backend and AI workflows, integrating advanced AI capabilities into scalable enterprise platforms. SDLC & Agile Practices: Familiar with More ❯

of-the-art tools and platforms. Essential Skills for the Role: Python (FastAPI): Proficiency in Python, particularly with the FastAPI framework, is vital to develop robust, scalable AI solutions. LangChain: Practical experience using LangChain to integrate and operationalize large language models. Google AI SDK & Azure Open AI SDK: Expertise with these SDKs to deploy and manage AI capabilities within GCP More ❯