251 to 275 of 399 Retrieval-Augmented Generation Jobs in the UK

fine-tuning and RAG. Design and implement the context layer-spanning knowledge graphs, vector search, and semantic retrieval-to create reliable, grounded RAG pipelines. Architect foundation model adaptation strategies, including dynamic, cost-and-performance-aware model routing and selection. Design, implement, and prototype high-throughput, low-latency inferencing ...

collaborate with product teams to deliver reliable AI tooling. The role requires strong Python, expertise with LLM frameworks, FastAPI, and Kubernetes, plus experience with RAG and tool orchestration in an enterprise setting. #J-18808-Ljbffr ...

working closely with senior stakeholders. What you'll need: Experience delivering AI/Generative AI solutions in a commercial environment Strong knowledge of LLMs, RAG and modern AI architectures Python and cloud platform experience (Azure preferred) Experience integrating AI solutions into enterprise applications Ability to explain complex technical decisions ...

than broad credentials. What you'll need to succeed 10-12 years in software or platform engineering, including recent delivery of production LLM applications (RAG, tool calling, agents, evaluations). Deep AWS expertise (IAM, Lambda, Step Functions, EventBridge, CloudWatch), infrastructure-as-code (Terraform or CDK), and CI/CD practice. ...

experience in APIs, integrations and distributed systems Proven experience designing, building and delivering solutions into production environments Experience with AI architecture and patterns, including RAG, tool calling and multi‐agent orchestration Experience building or deploying AI/LLM‐powered solutions and workflows Full‐stack software engineering skills, particularly Python, APIs ...

customer‐facing team in a cloud computing environment. Experience developing AI/Generative AI solutions utilizing AI tools and designing multi‐agent workflows and RAG systems. Experience in people management. Preferred qualifications Master’s degree or PhD in AI, Computer Science, or a related technical field. Experience in architecting ...

Have Passion for or experience in e-learning projects is a strong bonus Hands-on experience building AI-powered features (e.g. LLM integrations, RAG, AI-assisted user experiences) is a strong bonus Experience running and monitoring AI products in production: evals, output quality tracking, and observability specific to AI/ ...

Model programme for a central government department. Where the Lead Technical Architect sets direction, you turn it into working, production-grade systems - semantic search, RAG pipelines, and broader generative AI capability - integrated into complex Legacy and multi-cloud environments handling high-volume, sensitive public sector data. You'll work inside … eventually run these systems without long-term reliance on external suppliers. Key Responsibilities Hands-On Build & Delivery Design, build, and ship production AI components - RAG pipelines, retrieval and embedding infrastructure, orchestration logic, and integration layers - writing high-quality, tested, maintainable code and staying close to implementation rather than ...

Guide architecture across teams, helping reduce complexity and establish common patterns. Partner with AI and Retrieval teams to build scalable search and RAG capabilities. Lead complex cross‐team technical initiatives from design through production. Improve platform reliability, scalability, observability, and developer experience. Contribute hands‐on to critical technical … team technical initiatives. Experience with cloud infrastructure (AWS), Kubernetes, APIs, and modern backend development. Familiarity with AI‐powered systems, Retrieval‐Augmented Generation (RAG), search platforms, or vector databases. Strong programming experience in Python or another modern backend language. Comfortable influencing technical direction without direct ...

across microservices to identify and resolve failures in real‐time. Experience connecting agents to enterprise knowledge bases and optimizing Retrieval‐augmented generation (RAG) chunking to prevent hallucinations. Ability to travel up to 50% of the time. Track record of troubleshooting live, high‐traffic systems ...

pipelines. The engineer must also ensure solution quality through evaluation frameworks, ground-truth validation, and defined performance metrics. Key Responsibilities Design and buildGenAI/RAG-based applicationsusing data from multiple enterprise systems. Integrate withstructured and unstructured data sources, including databases, APIs, files, and document repositories. Develop pipelines toparse and extract ...

Significant experience shipping AI agents and LLM systems into production, with strong opinions on the modern toolkit — eval frameworks, agent orchestration, RAG pipelines, prompt engineering — and the tradeoffs between them. A track record of taking ambiguous problems from zero to one, raising the bar for the engineers around ...

LLMs, prompt design, and agent orchestration Strong Python skills and solid backend/API fundamentals Experience with data pipelines, embeddings, retrieval, or RAG systems Product‐driven mindset with comfort operating in ambiguity Ability to reason about latency, cost, reliability, and quality trade‐offs Excellent communication and ownership mentality ...

Python code Solid understanding of backend systems and APIs, even if not infrastructure-focused Experience working with data pipelines, embeddings, retrieval, or RAG-style systems Ability to reason about latency, cost, reliability, and quality trade-offs Product mindset You naturally think in terms of user outcomes, not just ...

scaling AI‐enabled products, internal platforms, workflow tools, agents, or automation capabilities. Experience with AI product evaluation, model performance, responsible AI, prompt design, RAG, knowledge graphs, or agent orchestration. Experience in financial services, capital markets, asset servicing, banking, payments, operations, risk, compliance, or data‐intensive enterprise environments. Experience working ...

/AI, guiding customers and the Field Engineering team to design production‐grade AI solutions on the Databricks platform. You will drive GenAI initiatives, RAG architectures, agentic systems, AI observability, and NLQ of structured data, while mentoring peers and influencing the platform roadmap. Some travel may be required. #J ...

move fast and safely at scale. You will collaborate with backend and AI engineers to deliver production-grade AI capabilities, including Agentic AI, RAG, embeddings, and vector search, while building secure deployment pipelines and observability across systems. #J-18808-Ljbffr ...

/ML projects, mentor teammates, and ship models into production, balancing quick wins with robust engineering. Focus areas include AI/OCR workflows, RAG systems, and deploying models on AWS with SageMaker/Bedrock. This hybrid role requires 1–2 days in Central London per week and right to work ...

Engineer | £80,000 - £110,000 | London/Hybrid | Python | LLM | GenAI | RAG | AI Agents | Looking to join one of the fastest growing AI companies in Europe Want to have autonomy and own end-to-end solutions I am currently supporting a brilliant AI company who are looking ...

through technical leadership, innovation initiatives and mentoring. Responsibilities Lead engineering teams delivering Generative AI solutions. Design and build scalable full‐stack AI applications. Develop RAG pipelines, AI agents and LLM‐powered platforms. Define technical architecture and engineering best practices. Drive delivery from proof‐of‐concept to production deployment. Engage directly ...

build business cases, and define practical implementation roadmaps. Provide technical leadership across AI technologies Lead across modern AI architectures, Generative AI, Agentic AI, LLMs, RAG, Data Platforms, Cloud AI Services, and enterprise integration patterns. Remain hands-on where required Provide architectural guidance, technical assurance, innovation leadership, and support the resolution ...

Skills Strong banking domain expertise (retail, lending, payments, contact center) Expertise in Conversational AI/IVR and GCP architecture Knowledge of Agentic AI, RAG, and API-led integrations Exceptional stakeholder engagement and influencing skills Experience 1218+ years in enterprise architecture/solution design Proven experience in onsite client-facing roles ...

with cloud‐native application architectures (containers, microservices, serverless) and the security considerations for these environments. Familiarity with AI‐integrated application risk, including LLM integration, RAG architectures and AI‐assisted development tooling. Strong written and verbal communication skills, able to translate technical risk into business language and vice versa. Benefits ...

experience with software development using Python or similar coding languages. Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions. Experience taking production-grade AI-driven solutions from conception to launch, and architecting AI systems on cloud platforms (e.g. ...

issues, and have informed opinions on AI/ML system architecture AI‐serious: you've led or contributed to shipping LLM‐powered systems (agents, RAG, evaluation infrastructure) in production, and you have opinions on what works Comfortable with the player‐coach archetype: you don’t want to stop being technical ...