16 of 16 Multi-Agent System Jobs in England

solutions into enterprise environments. This is a hands-on, customer-facing role focused on building and deploying RAG pipelines, multi-agent systems and fine-tuned LLM applications using tools such as LangChain and LangGraph. You will embed with client teams … technical stakeholders Requirements Strong experience building and deploying LLM/Generative AI solutions Hands-on with LangChain, RAG, multi-agent systems Cloud ML deployment experience (AWS, Azure or GCP) DevOps tooling: Kubernetes, CI/CD, GitHub, GitOps Customer-facing technical delivery ...

production-grade AI systems for external clients, including: LLM applications using modern orchestration patterns, prompt frameworks and evaluation loops Multi-agent architectures, including planning, delegation, safety constraints and monitoring Retrieval and vector-based systems, embeddings, structured reasoning and semantic workflows … applied data engineering Strong hands-on experience with LLMs, embeddings, RAG, retrieval stacks and vector databases Experience designing or implementing multi-agent systems or tool-calling frameworks Strong Python skills with experience building production-grade systems Experience working across ...

architectural choices directly shape what we ship to clients.Day to day, you'll work across the full delivery lifecycle: designing multi-agent orchestration pipelines and RAG patterns on the backend, building performant React frontends that surface AI-driven insights, and collaborating with … with generative AI components, ensuring high availability and resilience across Azure-hosted environments Architect complex backend pipelines in Python, incorporating multi-agent systems, advanced RAG patterns, and prompt engineering strategies within Azure’s enterprise AI ecosystem (Azure OpenAI, Azure AI Search ...

Anthropic, Azure OpenAI) Familiarity with agent frameworks such as LangChain, LlamaIndex, AutoGen, or similar Experience building multi-agent systems and orchestration workflows Knowledge of prompt engineering and optimization techniques Technical Depth C#/.NET background for enterprise ...

Requirements Proven experience building and deploying LLM/Generative AI applications Strong hands-on expertise with LangChain, RAG architectures, and multi-agent systems Experience deploying ML solutions in cloud environments (AWS, Azure, or GCP) Familiarity with DevOps practices and tooling: Kubernetes … experimentation, and taking ownership of delivery in fast-paced environments. Apply now to help shape the next generation of enterprise AI and agentic system deployments. ...

with multi‐agent frameworks/systems and an understanding of multi‐agent systems and their applications in complex problem‐solving scenarios. Experience in working with variety of data bases ...

doing You’ll take ownership of core parts of the agentic AI stack end-to-end, including: Designing and deploying multi-agent systems Building and scaling RAG-based architectures Developing evaluation frameworks focused on performance, reliability, and safety Delivering AI-powered ...

same stack, and the same mission. The difference is your specialism. ────────────────────────── 1. Staff SE – Agentic AI Leading the design of multi-agent systems and LLM orchestration patterns. If you've built and shipped complex agentic workflows end-to-end, this ...

behind Revax’s AI-native platform for professional services. You’ll design, optimise, and scale the systems that power multi-agent drafting, knowledge graphs, and auditable AI workflows used by accountants, tax advisors, and consultants at leading firms. This … real-world data · Design and implement APIs using FastAPI for AI workflows and product features · Build and scale LangGraph-based multi-agent systems with checkpointing, retries, and observability · Integrate and scale vector search, knowledge graphs (Neo4j), and associated retrieval pipelines into ...

What You’ll Be Doing Designing end-to-end AI architectures (RAG, agentic workflows, AI-powered applications) Leading architecture across multi-agent systems and advanced GenAI platforms Building scalable, secure solutions on AWS Defining LLMOps, evaluation frameworks, and AI governance Working ...

applied AI engineering, driving the design, evaluation, and governance of advanced AI solutions Proven experience designing, evaluating and governing multi-agent systems, incorporating memory structures, tool-use orchestration, planning algorithms and autonomy controls. Demonstrated ability to benchmark LLM performance, evaluate model ...

. Key Responsibilities: Lead AI strategy and executive advisory engagements Architect enterprise-grade Generative AI and Agentic AI solutions Design multi-agent systems, orchestration frameworks and RAG pipelines Own end-to-end solution architecture from discovery to implementation Shape ...

Role description: This is not a slide-making or prompt-engineering role. We are looking for someone who has built multi-agent AI systems that run in production - not demos, not pilots that died after a sprint. You will anchor … Responsibilities Delivery & Architecture Own end-to-end delivery of AI-native programs - from architecture through production deployment Design and build multi-agent orchestration systems using LangChain, LangGraph, CrewAI, or equivalent Integrate agent systems with enterprise surfaces ...

smooth performance across devices Ensure accuracy, scalability, and consistency across the digital twin pipelines Nice to have: Building agents/multi-agent systems Developing Retrieval-Augmented Generation (RAG) systems Agentic frameworks: LangGraph/LangChain Deployed LLMs at enterprise scale Modis ...

Build a scalable semantic layer —the single source of truth for people and AI AI in action: Create and orchestrate multi-agent systems that automate workflows and generate insights instantly Commercial impact: Equip teams across Sales, Strategy, and Revenue with real ...

Agentic AI frameworks and concepts (e.g., AutoGen, CrewAI, LangGraph, OpenClaw, NemoClaw, agent planning, tool use integration, multi-agent collaboration). AI Architecture: Deep understanding of AI/ML system architecture patterns, including microservices, event-driven architectures … patterns specific to RAG (Retrieval-Augmented Generation), Graph RAG, Agentic RAG, and multi-agent systems. Data: Knowledge of industry approaches to data engines and data labeling like Scale.ai and Mercor. Experience with auto data-labelling and synthetic data generation techniques ...