226 to 250 of 498 Retrieval-Augmented Generation Jobs in London

Anthropic APIs. The role requires 3-5 years of experience, strong Python/TypeScript skills, and hands-on experience with LLMs, vector databases, and RAG pipelines. #J-18808-Ljbffr ...

Experience with AI evaluation frameworks Ability to code independently Strong problem-solving and communication skills Comfortable working autonomously. Technical Experience AI/LLM Evaluation, RAG Evaluation, Ragas, Agentic AI Evaluation, Evaluation Harnesses, LLM Testing and Benchmarking, Prompt Engineering, Python Development, Model and Agent Integration. Ideal Background AI Evaluation Engineer, Applied ...

Experience with AI evaluation frameworks Ability to code independently Strong problem-solving and communication skills Comfortable working autonomously. Technical Experience AI/LLM Evaluation, RAG Evaluation, Ragas, Agentic AI Evaluation, Evaluation Harnesses, LLM Testing and Benchmarking, Prompt Engineering, Python Development, Model and Agent Integration. Ideal Background AI Evaluation Engineer, Applied ...

experience Strong React.js, Node.js and TypeScript experience Next.js and RESTful APIs GCP including Cloud Run Kubernetes and Terraform AI/LLM integrations, AI agents, RAG or agentic workflows Unit, integration and end-to-end testing CI/CD, secure coding and DevSecOps Experience building scalable, high-traffic applications Strong ...

escalation point Experience with RBAC, data masking, Photon, cluster optimisation or cost management would be beneficial Exposure to Mosaic AI, Vector Search, model serving, RAG or Lakebase would be advantageous ...

Fargate or similar orchestration technologies * NoSQL and relational databases * Messaging and integration services * Cloud storage solutions - Practical experience applying AI technologies such as LLMs, RAG, AI agents and automation within software solutions. In accordance with the Employment Agencies and Employment Businesses Regulations 2003, this position is advertised based upon ...

Qualifications Engineering experience with cloud-native systems (APIs, microservices, containerization, serverless). Deep expertise in designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments. Experience with AI platforms — OpenAI, Claude, Vertex AI, plus open-source models — including building abstraction layers to manage multi-provider pipelines. ...

looking for Hands-onengineering experiencewith cloud-native systems(APIs, microservices, containerization, serverless) Demonstratedexpertisein designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) Demonstrated end-to-end delivery ownership in a client-embedded environment, with experience building relationships with senior stakeholders We are hiring across Europe at multiple levels. ...

crash clustering, log anomaly detection, failure classification, or intelligent alert prioritization Familiarity with LLMs or generative AI tooling in an engineering context — prompt engineering, RAG pipelines, or AI-assisted debugging workflows Prior coursework or project experience in OS internals, systems programming, or low-level debugging Familiarity with Apple platform internals ...

years of hands‐on software engineering. Comfortable working in C++, with Python alongside it. Practical experience using LLMs as an applied tool: prompting, guardrails, RAG, evaluation, and reasoning about model behaviour. You've built, or extended a model and integrated it meaningfully into a real product, close to the surface ...

code You have experience working with large-scale, unstructured text data and LLMs - including prompt design, evaluation (e.g., golden sets, LLM-as-judge), and RAG patterns You have demonstrated ability to mentor junior data scientists, balance speed, risk, and analytical rigor You have experience with advanced visualization techniques/best ...

practice Basic Qualifications Engineering experience with cloud-native systems (APIs, microservices, containerization, serverless). Deepexpertisein designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments. Experience with AI platforms OpenAI, Claude, Vertex AI, plus open-source models including building abstraction layers to manage multi-provider pipelines. ...

work forward independently. What makes you stand out? Experience building AI/LLM-enabled products in production or controlled enterprise environments (e.g. agentic workflows, RAG, prompt engineering, evaluation and governance patterns) Experience with React/Next.js and modern web tooling. Familiarity with document/NoSQL stores (e.g. MongoDB) and data ...

code You have experience working with large-scale, unstructured text data and LLMs — including prompt design, evaluation (e.g., golden sets, LLM‐as‐judge), and RAG patterns You have demonstrated ability to mentor junior data scientists, balance speed, risk, and analytical rigor You have experience with advanced visualization techniques/best ...

communication skills; able to translate complex technical concepts for business audiences and vice versa Preferred Qualifications Experience with AI/LLM‐adjacent products — including RAG pipelines, knowledge graphs, embedding models, or MCP/API integrations powering AI applications Familiarity with Agentive AI architectures and how knowledge layers support intelligent automation ...

must demonstrate differentiated, hands-on Agentic AI and GenAI expertise: 1/Proven experience architecting and building agentic AI systems - including multi-agent orchestration, RAG pipelines, and prompt engineering - not theoretical familiarity but demonstrated delivery of working prototypes and customer-facing demos. 2/Proficiency using AI-powered coding assistants ...

covers the full AI/ML engineering lifecycle, from discovery to deployment and monitoring. Responsibilities include Designing and implementing agentic systems using techniques spanning RAG, grounding, prompt engineering, and orchestration on a GCP‐first stack. Building and maintaining production ML pipelines and services for non‐GenAI use cases (e.g. recommender … Terraform), experiment tracking, model registry, and monitoring. Proven experience deploying and operating ML systems in production (batch and real‐time). Familiarity with RAG architectures, prompt engineering, guardrails and evaluation techniques. Previous experience developing agentic capabilities, such as agent skills, MCP servers, tool usage. Strong grasp of security, privacy ...

across a range of AI and cloud projects. Responsibilities will include: Designing and delivering Generative AI and Machine Learning solutions Building applications using LLMs, RAG frameworks and AI orchestration tools Working directly with clients to understand business challenges and define AI-driven solutions Leading architecture and engineering decisions across Azure … experience building AI-powered applications and deploying them into production Software Engineering or Product Engineering background Strong Python development skills Experience working with LLMs, RAG architectures and modern AI frameworks Multi-cloud experience across Azure, AWS and GCP Strong understanding of cloud-native architectures and scalable application design Experience with ...

concept work. Reviewing code and technical outputs to maintain high engineering and quality standards. Designing and delivering Generative AI and LLM solutions , including RAG architectures, prompt engineering, vector search and integration with Azure AI services. Defining engineering approaches for LLM applications, agentic AI systems, machine learning solutions and AI platforms … TensorFlow . Practical experience with Databricks , including MLflow and Spark-based data processing. Hands-on knowledge of Generative AI and LLMs , including prompt engineering, RAG architectures and vector search. Strong understanding of MLOps , including CI/CD, model testing, deployment, monitoring and model lifecycle management. Cloud experience, ideally within ...

routing, rate limiting, cost management, modelselectionand version control across multiple model providers. AI solution design Design retrieval and reasoning architectures including RAG, corrective RAG (CRAG), and agentic patterns, selecting the right approach for a given business problem. Architect agent harnesses and their components — planning, memory, tool use, statemanagementand … Working understanding of how AI integrates with major operational systems such as SAP,Salesforceand ServiceNow. Hands-on knowledge of agentic AI, LLM gateways, MCP, RAG/CRAG, agent harnesses, evaluations and guardrails, and AI observability. Demonstrable experience designing and leading data & AI transformationprogrammes, andbuilding high-performing teams in complex stakeholder ...

reviews, oversee integration quality, and ensure adherence to enterprise performance, security, and scalability standards. Direct implementation of LLM orchestration, Retrieval‐Augmented Generation (RAG), and Human‐in‐the‐Loop (HITL) workflows on live projects. Package learnings into reusable delivery playbooks to enhance speed and quality … platform, including advanced integration patterns, core architecture design, and performance tuning. Practical knowledge of Agentic AI architecture, multi‐agent orchestration, prompt engineering, vector databases (RAG), and data privacy guardrails. Strong background in Agile/Scrum delivery, defining MVP boundaries, and creating realistic work‐breakdown structures for SOWs. Dual‐mindset agility ...

services with existing enterprise applications including Microsoft 365, Dynamics and other University systems. Building secure AI solutions using Retrieval Augmented Generation (RAG), prompt engineering and orchestration techniques. Develop Python-based services, APIs and automation scripts to support AI products. Use Power BI to develop ...

Linux-based environments and proficient in managing containerized workloads and automated CI/CD pipelines. Advanced RAG: Experience building production-ready Retrieval-Augmented Generation systems, including vector database management and semantic search optimization. Preferred Qualifications Experience in the insurance or financial services sector. Deep ...

infrastructure, Kubernetes, and infrastructure as code. Familiarity with LLM APIs and the surrounding ecosystem, including model providers, gateways, MCP, agent frameworks, and retrieval-augmented generation. Experience building observability into distributed systems using metrics, tracing, and logging. A security-conscious approach, including experience with authentication, authorization ...

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 ...