1 to 25 of 39 Remote/Hybrid Retrieval-Augmented Generation Jobs in London

knowledge graphs, and episodic/audit memory. Ensure coherent retrieval strategies across layers. Retrieval-Augmented Generation (RAG) and CAG (Cache Augmented) Architecture : Define architectural patterns for end-to-end RAG pipelines, including chunking, embedding, vector search (e.g., Azure Cognitive Search … least one other. • GenAI & LLM Depth : Demonstrated experience architecting and guiding solutions using GenAI platforms (e.g., Azure OpenAI, Vertex AI, or AWS Bedrock). • RAG & Orchestration : Proven experience designing complex RAG pipelines. • Model Fine-tuning : Experience with instruction tuning or fine-tuning strategies for LLMs. • Leadership & Advisory Skills : Exceptional communication ...

accelerate software development (using Github Copilot Coding Agents or similar) Desirable: Experience with prompt engineering, Retrieval-Augmented Generation (RAG), and graph databases Familiarity with multi-agent LLM systems and agentic platforms (e.g., AutoGen, CrewAI), and experience deploying LLM-based applications Experience with tools such ...

help drive innovation across cutting-edge AI initiatives — from chatbots and voice assistants to advanced retrieval-augmented generation (RAG) systems and agentic workflows. The Role You’ll work closely with the AI Engineering and Data Science teams to: Develop and prototype AI-driven solutions … across customer-facing and internal applications. Build and optimise LLM-based assistants , RAG pipelines , and agentic AI workflows . Collaborate on the architecture and deployment of scalable AI solutions (with support from engineering). Partner with stakeholders to translate business needs into practical, intelligent systems. Mentor junior team members ...

application developers to translate business objectives into high-quality AI outcomes. A working understanding of Retrieval-Augmented Generation (RAG) is essential to ground model responses in authoritative enterprise content and to reduce hallucinations. This role blends hands-on engineering with rigorous experimentation, evaluation … . Utilize and manage RESTful APIs/SDKs to integrate model calls within web services, back-end jobs, and enterprise applications. Design and implement RAG pipelines (chunking, embeddings, indexing, ranking/citation policies) to ground responses in approved content stores. Establish offline/online evaluation frameworks (accuracy, safety, faithfulness, latency ...

intuitive user experiences. Work closely with AI researchers and ML engineers to integrate LLMs, Retrieval-Augmented Generation (RAG), and automation into production-ready applications. Ship robust, minimal-dependency code that performs efficiently in enterprise environments. Continuously iterate and refine AI-driven products, balancing user ...

intuitive user experiences. Work closely with AI researchers and ML engineers to integrate LLMs, Retrieval-Augmented Generation (RAG), and automation into production-ready applications. Ship robust, minimal-dependency code that performs efficiently in enterprise environments. Continuously iterate and refine AI-driven products, balancing user ...

Engineer will be responsible for developing secure, scalable, and high-performing AI solutions, including document intelligence workflows, generative AI applications, and retrieval-augmented systems. This includes working on model orchestration, prompt engineering, document extraction pipelines, and system integrations with enterprise platforms such … intelligent document processing pipelines using Azure AI Doc Intelligence for tasks such as classification, extraction, OCR, and automated document workflows. Implement RAG (Retrieval-Augmented Generation) architectures using Azure Search or vector databases. Integrate AI capabilities with enterprise systems through REST APIs, Azure Functions, Logic ...

following skills and experience: Strong Python and SQL experience, ideally with Databricks Experience with ML and LLM tools (e.g., scikit learn, XGBoost, PyTorch, TensorFlow, RAG, APIs) Comfortable with data engineering basics and MLOps practices Experience working with cloud platforms such as Azure, AWS, or GCP Benefits The successful Senior ...

microservices or serverless architectures Practical exposure to machine learning plus generative AI models including LLMs SLMs vision models Familiarity with AI frameworks such as RAG LangChain LlamaIndex plus agentic frameworks Hands-on cloud experience within Azure or AWS plus CI/CD tooling and Git Desirable Skills ...

including AI and ML services Solid foundation in software engineering principles for scalable, production-grade systems Experience designing and delivering enterprise AI solutions including RAG-based architectures using vector databases Proven experience delivering full-stack AI or ML systems within enterprise environments Strong understanding of advanced agent architectures, reasoning systems ...

including AI and ML services Solid foundation in software engineering principles for scalable, production-grade systems Experience designing and delivering enterprise AI solutions including RAG-based architectures using vector databases Proven experience delivering full-stack AI or ML systems within enterprise environments Strong understanding of advanced agent architectures, reasoning systems ...

handling both single-step LLM tasks and complex multi-step agentic workflows. As AI Engineer you will: Architect and implement the LLM, agentic and RAG modules that power the platform’s low-code environment. Build primitives, abstractions and orchestration components that allow clients to construct robust agentic workflows with deterministic ...

/ML solutions in production Strong programming skills in Python and SQL Experience with classical machine learning and LLMs/generative AI (prompting, RAG, APIs) Solid software engineering fundamentals Experience working with cloud platforms Benefits The successful Senior AI Engineer will receive the following benefits: Salary ...

Python and JavaScript Hands-on experience with backend frameworks and modern frontend frameworks Deep knowledge of Generative AI and agent-based AI frameworks including RAG and orchestration patterns Strong experience working with vector databases and AI-specific data stores Solid understanding of AI and ML models, LLMs and generative ...

both technical and senior stakeholders. Comfortable working across multiple teams and translating business needs into technical solutions. Desirable Experience: Exposure to Generative AI, LLMs, RAG techniques, or related frameworks. Experience with Microsoft Power Platform technologies. Front-end development using modern frameworks such as React, Angular, or Vue. Awareness of Edge ...

autonomous AI What You’ll Need: A strong foundation in data science and machine learning Hands-on use of modern AI tools (LLMs, RAG, LangChain, co-pilots, agentic workflows) Curiosity and eagerness to learn in a fast-moving AI landscape Experience collaborating with stakeholders and translating business problems into technical ...

have: Strong Python engineering skills for backend services and API integrations. Hands-on experience working with LLMs and related tooling, including: LLMs RAG patterns, vector databases, function calling, and prompt design. Solid experience with Google Cloud Platform, ideally including: Vertex AI, Model Garden, BigQuery, Cloud Run or Cloud Functions Ability ...

complex technical risks into actionable guidance for senior stakeholders. Preferred Skills Certifications: CISSP, CCSP, OSCP, or equivalent cloud security credentials. Advanced AI: Experience with RAG pipelines, vector databases, and AI model fine-tuning. Regulatory Knowledge: Understanding of the EU AI Act or ISO/IEC 42001. DevSecOps: Familiarity with automation ...

Strong technical background with hands on experience building AI systems, prototypes, or proof of concepts - Experience with machine learning and generative AI, ideally including RAG based solutions - Python experience and solid understanding of core AI concepts - Ability to translate technical AI solutions into clear, simple messages for non technical stakeholders ...

point of application Strong programming skills in Python Understanding of one or more of the following: Agentic AI systems and architectures Graph RAG or Traditional RAG Vision Language Models or Computer Vision Audio or signal processing Experience with Docker, Linux/Unix CLI, Git, and testing frameworks Experience with ...

assessments, and delight users Work with industry experts to turn messy claim processes into AI-driven workflows Deploy solutions using LLMs, LangChain, and RAG at scale What we're looking for 2+ years in AI/ML engineering or research Proven track record taking projects from idea → production → impact Bias ...

TypeScript-based applications Understanding of semantic/graph data (e.g., RDF triples, SPARQL , Amazon Neptune ) Ability to validate AI-supported features, including LLM/RAG behaviours via Amazon Bedrock Knowledge of performance testing for serverless architectures and WCAG 2.2 AA accessibility Familiarity with GDS Standards and testing in the public ...

development, along with experience in Docker , PostgreSQL , or DSL/Compilers . Professional or enthusiastic personal experience with LLMs and their ecosystems (e.g., Langchain, RAG, or observability tooling). You are a "creative experimentalist" who can balance the need for speed with long-term engineering maintenance. A commitment to building ...

developing machine learning models across forecasting, classification, and anomaly detection use cases. Building and deploying GenAI and LLM-enabled solutions, including prompt-driven and RAG-based architectures. Running structured experimentation cycles, defining hypotheses, designing experiments, and evaluating results within governance constraints. Transitioning validated experiments into production-ready systems in collaboration … tooling such as MLflow, Weights & Biases, or Data Version Control. Experience developing or integrating LLM and GenAI applications, including prompt engineering and retrieval-augmented generation. Ability to work across disciplines and communicate technical findings clearly. A pragmatic mindset and good judgement about when machine learning ...

translate business outcomes into actionable AI roadmaps and solution designs. You will also guide implementation teams on best practices for prompt engineering, grounding strategies, RAG patterns, observability, MLOps, and Responsible AI-ensuring solutions are robust, compliant, and production ready. This role suits a hands on architect who can balance strategic … Foundry, Azure OpenAI, and Azure AI Document Intelligence for enterprise use cases (IDP, GenAI copilots, knowledge retrieval). Establish standards for RAG architectures (vectorization, indexing, chunking, grounding, citation policies) using Azure Search or vector enabled data stores. Create architectural decision records (ADRs), guardrails, and reusable blueprints for solution ...