1,151 to 1,175 of 2,427 LLM Jobs in the UK

hackajob is partnering directly with Raytheon UK to hire for this role. Date Posted: 2026-05-28 Country: United Kingdom Location: Gloucester, South Gloucestershire Position Role Type: Hybrid Raytheon UK has a perm opportunity for ...

Full job description Job Title: Forward Deployed Engineer Company: 167 Solutions Location: Bristol, London, Manchester Salary: £50,000 - £70,000 per annum (DOE) Contract Type: Permanent Experience Level: Minimum 3 years' commercial experience Start Date ...

Senior Product Engineer (AI & Full Stack) Location: Central London (Hybrid - 2-3 days per week) Salary: £65,000 - £75,000 + Bonus + Excellent Benefits Job Type: Permanent Please note: Applicants must have the full ...

Senior Product Engineer (AI & Full Stack) Location: Central London (Hybrid - 2-3 days per week) Salary: £65,000 - £75,000 + Bonus + Excellent Benefits Job Type: Permanent Please note: Applicants must have the full ...

platform and turn successful approaches into reusable tools and accelerators. What you'll be working on: Building AI solutions using RAG, AI agents, LLM integrations and MCP Taking solutions from prototype through to production, including security, scalability, guardrails and observability Building AI evaluation and testing frameworks to measure accuracy … TypeScript or similar, with experience across multiple modern languages Full-stack capability, including back-end and data engineering Proven experience building and shipping production LLM/AI applications Hands-on experience with RAG, AI agents, prompt engineering, tool orchestration and MCP Experience with AI evaluation, testing, datasets and guardrails Client ...

surfaced early Translate program/product roadmap into solution designs and detailed architecture specs for Commercial Digital Solutions Define architecture patterns for GenAI/LLM-based solutions (RAG, agentic workflows, model orchestration), evaluate build-vs-buy for AI capabilities Architect integration patterns between Commercial systems and the enterprise data platform … looking for? Hands-on experience architecting multi-step agentic AI workflows, including agent-to-agent orchestration, context and memory management, and integration of LLM-based reasoning with enterprise data sources and APIs Experience evaluating vendor and open-source agentic AI platforms against enterprise requirements - including assessing agent reliability, hallucination risk ...

Build and own RAG pipelines: embeddings, chunking strategy, vector search, context window engineering and tuning against real quality targets Integrate and abstract across multiple LLM providers — OpenAI, Anthropic, Vertex AI, and open-source models — with fallback routing, token, cost, and latency management Implement LLMOps in production: eval harnesses with real … production environment — non-negotiable Demonstrated experience with agentic orchestration frameworks: LangGraph, CrewAI, AutoGen, or equivalent — at production depth, not tutorial level Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management, latency and cost tradeoffs RAG pipeline ownership: embeddings, chunking strategy, vector databases ...

expertise across Python, C#, TypeScript/React, advanced SQL, and complex data modelling, working with both structured and unstructured data. Proven applied AI/LLM experience, shipping production solutions using RAG, embeddings/vector search, agentic workflows, evaluation frameworks, and guardrails. Hands-on cloud and infrastructure expertise across Azure … financial data providers (Bloomberg, LSEG/Refinitiv, FactSet, S&P Capital IQ, PitchBook, Preqin, MSCI), parsing financial documents at scale, and/or optimising LLM performance, cost, and latency. Exposure to enterprise data architectures (Kafka/Event Hubs, lakehouse, Snowflake, Oracle) and experience distributed international teams. Location & Work Model London ...

semi-autonomous systems). Establish quality, evaluation, and governance frameworks for non-deterministic and AI-driven systems, including test and evaluation strategies for LLM-based and agentic features. Set enterprise standards for validating AI-generated code, ensuring it meets Fourth’s quality, security, and maintainability requirements. Partner with Engineering, Product … organisation, mentoring and uplifting QA capability globally. Key Skills and Competencies Strong understanding of the testing and quality challenges of non-deterministic, ML/LLM-based, and agentic systems, including evaluation, guardrails, and the regression of model-driven behaviour. Proven experience defining and implementing QA strategy at an organisational ...

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support ...

that improve delivery efficiency Developing reusable AI capabilities and accelerators Supporting analysis, reporting and knowledge management initiatives Technical Responsibilities Python development AI agent orchestration LLM applications and RAG solutions API integrations Data pipelines Cloud-based development Application development Workflow automation What We're Looking For Essential 2-4 years' software … engineering experience Strong Python development skills Experience working with GenAI/LLM technologies Experience developing software applications rather than purely analysing data Understanding of AI workflows, agent frameworks or automation tooling Experience delivering real-world software solutions Strong problem-solving ability Excellent communication skills Highly Desirable AI Agents/Agentic ...

Build and own RAG pipelines: embeddings, chunking strategy, vector search, context window engineering and tuning against real quality targets Integrate and abstract across multiple LLM providers OpenAI, Anthropic, Vertex AI, and open-source models with fallback routing, token, cost, and latency management ImplementLLMOpsin production: eval harnesses with real quality metrics … production environment non-negotiable Demonstrated experience with agentic orchestration frameworks:LangGraph,CrewAI,AutoGen, or equivalent at production depth, not tutorial level Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management,latencyand cost tradeoffs RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context ...

Build and own RAG pipelines: embeddings, chunking strategy, vector search, context window engineering and tuning against real quality targets Integrate and abstract across multiple LLM providers OpenAI, Anthropic, Vertex AI, and open-source models with fallback routing, token, cost, and latency management ImplementLLMOpsin production: eval harnesses with real quality metrics … production environment non-negotiable Demonstrated experience with agentic orchestration frameworks:LangGraph,CrewAI,AutoGen, or equivalent at production depth, not tutorial level Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management,latencyand cost tradeoffs RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context ...

pipeline standards across engagements:establishchunking and embedding strategies, set quality benchmarks, and ensure metric-backed tradeoff decisions are documented and transferable Set multi-LLM integration standards: vendor-agnostic architecture by default, fallbackroutingand cost governance as standard design practice across providers including OpenAI, Anthropic, Vertex AI, and open-source models OwnLLMOpsatprogrammescale … production environment non-negotiable Demonstrated experience with agentic orchestration frameworks:LangGraph,CrewAI,AutoGen, or equivalent at production depth, not tutorial level Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management,latencyand cost tradeoffs RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context ...

pipeline standards across engagements:establishchunking and embedding strategies, set quality benchmarks, and ensure metric-backed tradeoff decisions are documented and transferable Set multi-LLM integration standards: vendor-agnostic architecture by default, fallbackroutingand cost governance as standard design practice across providers including OpenAI, Anthropic, Vertex AI, and open-source models OwnLLMOpsatprogrammescale … production environment non-negotiable Demonstrated experience with agentic orchestration frameworks:LangGraph,CrewAI,AutoGen, or equivalent at production depth, not tutorial level Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management,latencyand cost tradeoffs RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context ...

pipeline standards across engagements:establishchunking and embedding strategies, set quality benchmarks, and ensure metric-backed tradeoff decisions are documented and transferable Set multi-LLM integration standards: vendor-agnostic architecture by default, fallbackroutingand cost governance as standard design practice across providers including OpenAI, Anthropic, Vertex AI, and open-source models OwnLLMOpsatprogrammescale … production environment non-negotiable Demonstrated experience with agentic orchestration frameworks:LangGraph,CrewAI,AutoGen, or equivalent at production depth, not tutorial level Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management,latencyand cost tradeoffs RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context ...

unsanctioned AI usage without blocking it entirely. The team applies AI in how it builds as well — code assistants, AI-assisted test generation, and LLM-supported log and incident analysis — so you will be using these tools alongside defending against their misuse. Outcomes & KPIs Key Outcome(s) Owned: Reliable, performant … BTech, or Masters), or equivalent practical experience. Practical fluency with AI coding and debugging assistants, including sound judgement about when to rely on LLM output and when to verify it independently. Self‐directed approach to problem solving, with the ability to pick up unfamiliar systems and technologies with limited guidance. ...

unsanctioned AI usage without blocking it entirely. The team applies AI in how it builds as well - code assistants, AI-assisted test generation, and LLM-supported log and incident analysis - so you will be using these tools alongside defending against their misuse. Outcomes & KPIs Key Outcome(s) Owned: Reliable, performant … BTech, or Masters), or equivalent practical experience. Practical fluency with AI coding and debugging assistants, including sound judgement about when to rely on LLM output and when to verify it independently. Self-directed approach to problem solving, with the ability to pick up unfamiliar systems and technologies with limited guidance. ...

delivery plans. Design and deliver solutions Build AI and automation solutions using tools such as n8n, Tray.io, Workato, Zapier, Claude Code, and direct LLM APIs. Integrate with systems including Slack, Google Workspace, NetSuite, Salesforce, HubSpot, and the other tools used across the business. Develop robust workflows and integrations with appropriate … use. Hands-on fluency in low-code automation and integration platforms such as n8n, Tray.io, Workato, or Zapier, and in agentic patterns using LLM APIs from Anthropic, OpenAI, or similar. Confident hands-on coding in Python and TypeScript, comfortable building REST and webhook integrations, testing APIs (for example with Postman ...

AI Engineer - Tewkesbury Ontic, a leading licensor and manufacturer of complex engineering parts for the global aerospace and defence industries, are now recruiting for an AI Engineer to join our IT team, where you'll ...

Opportunity Brunswick is accelerating its use of AI to strengthen the advice we give clients and how we operate. We are looking for a Data Architect to join our AI Lab and shape the technical ...

About WRITER WRITER is where the world’s leading enterprises orchestrate AI-powered work. Our vision is to expand human capacity through superintelligence. And we’re proving it’s possible – through powerful, trustworthy AI that ...

scalable data solutions. Develop conceptual, logical, and physical data models. Design and implement modern AI-ready architectures, including RAG, vector databases, semantic search, and LLM integrations. Build and optimise data pipelines supporting both batch and real-time processing. Lead data integration, transformation, and migration initiatives. Establish and maintain data governance … data modelling experience across enterprise-scale environments. Experience building modern AI-enabled data architectures. Knowledge of Retrieval-Augmented Generation (RAG), vector databases, embeddings, and LLM integrations. Experience with both batch and streaming/real-time data processing. Strong understanding of data governance, quality management, lineage, and metadata frameworks. Excellent stakeholder ...

AI Engineer (.Net/AI) Location: Chester | Hybrid Working Permanent – Full-time Salary: £50,000–£60,000 We're working with a leading UK organisation that is continuing to invest in its internal AI capability ...

This job is with Kantar, an inclusive employer and a member of myGwork – the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly. We go beyond the obvious, using ...