251 to 275 of 498 Retrieval-Augmented Generation Jobs in London

/GCP). Ability to standardise and influence across teams. Nice to Have Lakeflow connectors, RBAC/masking, GenAI (Mosaic AI, Vector Search, RAG), Lakebase. #J-18808-Ljbffr ...

include Docker, Kubernetes, Azure, Infrastructure as Code, enterprise APIs, identity and access management, observability and modern DevOps practices. Exposure to AI platforms, LLM integration, RAG, agent frameworks or accelerated compute would be valuable, but we are primarily looking for strong architectural breadth and the ability to deliver complex technology ...

launch and architecting AI systems on cloud platforms (e.g., GCP). Experience building pipelines for structured and unstructured data using both vector databases and RAG‐like architectures to power enterprise AI solutions. Experience leading technical discovery sessions with customers. Active, or the ability to obtain, a Security Check ...

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. ...

Microsoft, Oracle, or similar platforms.* Background in change management, organisational design, or enterprise transformation* Experience with AI/ML concepts beyond LLMs, including NLP, RAG architectures, and model evaluation frameworks* A get-things-done mindset with strong bias for action — comfortable operating in unstructured fast moving environmentsUnleash Your PotentialWhen ...

launch and architecting AI systems on cloud platforms (e.g., GCP). Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions. Experience leading technical discovery sessions with customers. Active, or the ability to obtain, a Security Check ...

experience building and deploying production-grade software in Python; you write clean, testable, observable, scalable code You've built and deployed highly performant RAG and agentic applications, including agents that plan and execute multi-step tasks using patterns like ReAct or Plan-and-Execute You're deeply familiar with ...

hands-on experience building and deploying production-grade software in Python; you write clean, testable, observable, scalable codeYou've built and deployed highly performant RAG and agentic applications, including agents that plan and execute multi-step tasks using patterns like ReAct or Plan-and-ExecuteYou're deeply familiar with ...

essential. AI and ML platform architecture: experience designing AI/ML data infrastructure - including feature stores, model training pipelines, inference serving, and GenAI/RAG architectures grounded in proprietary content. Lakehouse and modern data platform design: strong knowledge of open table formats (Apache Iceberg, Delta Lake), zone architecture (landing, cleansed ...

such as Microsoft, Oracle, or similar platforms.Background in change management, organizational design, or enterprise transformationExperience with AI/ML concepts beyond LLMs, including NLP, RAG architectures, and model evaluation frameworksA get-things-done mindset with strong bias for action — comfortable operating in unstructured fast moving environmentsUnleash Your PotentialWhen you join ...

only custom-building where neither can deliver the required outcome. Support AI-enabled use cases across areas such as board reporting and narrative generation, management reporting summaries, finance process automation, forecasting and planning support, document ingestion and extraction, reconciliation and workflow automation, insight generation from structured … Have: Significant experience in applied AI, machine learning, data science, analytics engineering, automation or AI-enabled transformation. Strong understanding of generative AI, LLMs, RAG, agents, workflow automation, prompt engineering, evaluation and AI solution design. Familiarity with platforms such as Azure OpenAI, Microsoft Fabric, AWS Bedrock, GCP Vertex AI, Snowflake Cortex ...

solution delivery across client engagements — setting the bar for what production-ready looks like Review and challenge solution architectures end-to-end: LLM selection, RAG pipeline design, agentic workflow logic, evaluation frameworks, guardrails, cost modelling, and scalability Define and enforce the standards that separate a working POC from a deployable … build the talent to sustain it What You Will Bring Required — Proven GenAI Delivery A track record of shipping GenAI solutions into production — RAG pipelines, agentic workflows, LLM-powered applications, or multimodal systems — not just designing or advising on them Deep hands-on experience with the full GenAI delivery stack ...

solving some of the most exciting challenges and problem solving in enterprise AI rollout, spanning enterprise data pipelines, multi‐agent orchestration/workflows and RAG/SLM finetuning. Responsibilities Lead the technical solution design and implementation of large‐scale Agentic and Knowledge AI solutions for key enterprise clients. … solution delivery through reusable assets, frameworks, and documentation. Champion observability, monitoring, versioning and telemetry to ensure trustworthy and auditable AI agents. Stay current on RAG, LLM/SLM advancements, Agentic AI tooling, vector/graph databases, retrieval optimizations, multi‐modal systems, evaluation frameworks, GPU/Infra trends, fine ...

extend platform components-APIs, tooling, and bespoke logic Architecture & Standards: Architect robust, scalable, and compliant Agentic AI solutions across industries Lead multi-agent orchestration, RAG pipelines, and LLM/SLM integration design Champion observability, telemetry, and auditable AI standards Build and maintain reusable frameworks and asset libraries Team & Product: Help …/Vue, SQL, REST/GraphQL DevOps proficiency: Docker, Kubernetes, CI/CD, and major cloud platforms Expertise in LLMs, Prompt Engineering, Vector DBs, RAG pipelines, and Agentic orchestration frameworks Agentic/Integrations: MCP, Salesforce/SAP/Other Excellent communication with both technical and executive stakeholders Mentorship capability ...

audit-ready evidence generation. AI Enablement Implement AI/ML components within the model workflow, including LLM-based automation for model documentation generation and updating, intelligent data quality checks, and workflow assistance. Integrate AI tooling into a controlled, auditable, production-grade environment, with appropriate testing, monitoring, and governance … Solid AI/ML knowledge, including practical experience with machine learning libraries and/or LLM-based application development (e.g., API integration, prompt engineering, RAG-style document workflows). Strong experience with test automation, CI/CD, and modern software engineering practices (Git, code review, containerization). Experience working with ...

drive progress without being pushed Consulting or client-facing delivery experience is a strong advantage Exposure to GenAI applications in decisioning — personalised content generation, AI-driven offer selection Preferred Experience evaluating purpose-built decisioning solutions and contributing to capex/opex business cases Familiarity with real-time streaming … Technical Skills Advanced Python — writing clean, production-grade code, not just scripts (OOP, async, packaging, testing) Generative AI & LLM development — prompt engineering, fine-tuning, RAG pipelines, context window management Agentic AI frameworks — LangChain, LangGraph, AutoGen, or similar; building multi-step autonomous workflows Machine Learning — model development, training, evaluation, and deployment ...

drive progress without being pushed Consulting or client-facing delivery experience is a strong advantage Exposure to GenAI applications in decisioning — personalised content generation, AI-driven offer selection Preferred Experience evaluating purpose-built decisioning solutions and contributing to capex/opex business cases Familiarity with real-time streaming … Technical Skills Advanced Python — writing clean, production-grade code, not just scripts (OOP, async, packaging, testing) Generative AI & LLM development — prompt engineering, fine-tuning, RAG pipelines, context window management Agentic AI frameworks — LangChain, LangGraph, AutoGen, or similar; building multi-step autonomous workflows Machine Learning — model development, training, evaluation, and deployment ...

complex datasets, including time series data, from various sources (web APIs, databases, real-time streams). Leverage Generative AI platforms and frameworks agents, RAG, prompt and context engineering, evaluations and AI SDLC to deliver novel capabilities for the trading organisation. Tap into expertise in AWS cloud services (S3, Redshift … Selenium, Beautiful Soup and Requests, and machine learning libraries such as TensorFlow and PyTorch). Generative AI techniques including agents, skills, prompt engineering, RAG, AI SDLC, context engineering and evaluations. Mastery of SQL (including advanced optimisation techniques) and experience with relational databases (PostgreSQL preferred), including data modelling and design skills. ...

complex datasets, including time series data, from various sources (web APIs, databases, real-time streams). Leverage Generative AI platforms and frameworks agents, RAG, prompt and context engineering, evaluations and AI SDLC to deliver novel capabilities for the trading organisation. Tap into expertise in AWS cloud services (S3, Redshift … Selenium, Beautiful Soup and Requests, and machine learning libraries such as TensorFlow and PyTorch). Generative AI techniques including agents, skills, prompt engineering, RAG, AI SDLC, context engineering and evaluations. Mastery of SQL (including advanced optimisation techniques) and experience with relational databases (PostgreSQL preferred), including data modelling and design skills. ...

read-only pulls and progressing to bidirectional integration.**AI & Agentic Workflows*** Design, deploy and orchestrate agentic AI workflows using frameworks such as LangGraph; implement RAG pipelines, fine-tuned models and vector database integrations within the assigned business domain.* Operate the end-to-end delivery toolchain (AWS, GitLab, Docker, CI/… pipelines, REST/gRPC APIs and microservices architecture; identity and access management with OIDC/SAML and Azure Entra ID.* Experience with LLMs, RAG systems, prompt engineering and AI evaluation frameworks; familiarity with MLOps, model deployment and AI observability/guardrails; working use of AI-assisted development tools (e.g. Claude ...

privacy, cross-border data residency and audit requirements).* **AI Foundations:** Experience designing agentic or AI-enabled solutions, including patterns such as retrieval-augmented generation, tool and API integration, evaluation and human-in-the-loop design, would be a significant advantage, though ...

will play a key role in shaping how AI, agents, data, and ontology-driven business context become foundational capabilities across the next generation of Infor Manufacturing solutions. A Typical Day in the Life Includes: Defining the AI architecture, standards, and technical strategy for MES and the Manufacturing portfolio. … practices and technologies. Proven experience designing and delivering production AI solutions using Large Language Models (LLMs). Experience with Retrieval Augmented Generation (RAG), agentic architectures, AI orchestration frameworks, and tool-based AI systems. Experience integrating AI capabilities into enterprise software products. Experience designing APIs ...

e.g., Google Cloud Platform (GCP)). Experience building pipelines for structured and unstructured data using both vector databases and retrieval augmented generation (RAG) like architectures to power enterprise AI solutions. Experience leading technical discovery sessions with customers. Active, or the ability to obtain ...

meaningful way, whether for security testing, security research, or your own workflows* Enough understanding of how LLMs work (context windows, tool use, RAG, agentic chaining) to reason about where they can go wrong* Familiarity with AI testing tools or frameworks, even from personal research or experimentation, is a real plus ...

/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 ...