26 to 50 of 111 Retrieval-Augmented Generation Jobs in London

business requirements into technical architectures for GenAI and machine learning use cases. Implement prompt engineering, retrieval-augmented generation (RAG), and integration patterns with enterprise systems. Configure and optimise Microsoft Copilot Studio and other AI tools for internal use cases. Support the development of repeatable … Azure AI services. Strong ability to quickly prototype and test AI/ML solutions. Experience with APIs, vector databases, and integration patterns. Understanding of RAG, LLMOps, and prompt engineering. Job Offer Competitive daily rate (Inside IR35). Opportunity to work on innovative AI projects in the financial services industry. Temporary ...

scale machine learning algorithms and pipelines to production using big data technologies. Develop and deploy retrieval-augmented generation (RAG) systems and LLM-based applications. Design and evaluate A/B tests and communicate results across cross-functional teams. Define, implement, and monitor key performance ...

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 ...

managing multi-API workflows using tools like LiteLLM to ensure flexibility and resilience. Building sophisticated Retrieval-Augmented Generation (RAG) systems, leveraging advanced techniques like embeddings with Voyage AI, rerankers , and query enrichment . Designing and maintaining efficient data pipelines and vector storage solutions using … PyTorch. Proven experience building and deploying applications with LLM APIs such as OpenAI , Anthropic , Gemini , and DeepSeek . Hands-on experience with the full RAG pipeline, including vector embeddings , rerankers , and data indexing in databases like MongoDB. Practical knowledge of LLM fine-tuning, prompt engineering, and performance optimization. Familiarity with ...

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 ...

create high-quality pre-trained datasets, fine-tuned industry-specific Large Language Models(LLMs), and Retrieval-Augmented Generation (RAG) pipelines supported by vector databases. Our innovations can reduce Generative Artificial Intelligence(Gen AI) costs by up to 80% and bring Gen AI solutions ...

. Expertise in modern machine learning frameworks (scikit-learn, XGBoost, PyTorch, TensorFlow). Demonstrable experience delivering end-to-end ML and GenAI solutions (including RAG, LLMs, embeddings and vector databases). Strong knowledge of MLOps tooling and practices (MLflow, CI/CD, containerisation, monitoring). Experience leading, mentoring and developing ...

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 ...

fundamentals across the full lifecycle. Hands‐on experience with ML frameworks (scikit‐learn, XGBoost, PyTorch, TensorFlow, LightGBM). Strong exposure to LLMs, prompt engineering, RAG, and evaluation frameworks. Familiarity with MLOps tooling: CI/CD, experiment tracking, model registries, monitoring, containers. Please note: This role cannot provide VISA sponsorship . ...

pairs, decision trees, and intent-based chunks* Apply consistent taxonomy, tagging, and metadata to support retrieval-augmented generation (RAG) and AI consumption* Align knowledge with chatbot/model requirements, design prompt-ready content, analyze performance, and refine based on real user queries* Collaborate with ...

LangChain or Semantic Kernel. Solid understanding of ML concepts (supervised/unsupervised learning, transformers, CNNs/RNNs, model evaluation). Experience with prompt engineering, RAG pipelines, and model fine-tuning. The judgement to identify where AI adds value — and where it doesn’t. Experience deploying and operationalising LLMs (exposure ...

Communicate complex concepts to all stakeholders. At least 3 years of experience in AI solution engineering. Large Language Models experience including prompt engineering and RAG implementations. Expert data analytics, MLOps practices and API development. Desirable knowledge in Docker and Kubernetes ...

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 ...

least 2 AI domains: NLP, NLG, Computer Vision, Machine Learning, GenAI etc. Hands-on experience with fine-tuning/training LLMs and complex Graph RAG solutions (production environment). Technical Skills: Extensive knowledge of cloud services and tools (Azure & Databricks preferred), ), including Azure Machine Learning, Vertex AI, AI Search, Databricks ...

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 ...

Role 2: Applied AI Engineer Strong backend engineering experience (Python preferred) Deep knowledge of CI/CD, Docker, Kubernetes, and Git workflows Experience building RAG pipelines and AI agent orchestration systems Minimum 5 years of experience 📩 Interested? Message me here or email mmatysik@trg-uk.com ...

/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 ...

services on cloud infrastructure (GCP, AWS, and Azure) · High ownership mindset: you care deeply about reliability, correctness, and outcomes Strong Plus · Experience with LangGraph, RAG pipelines, vector databases, or knowledge graphs · Familiarity with Neo4j or graph-based data modelling · Experience working on AI-native or agentic systems · Comfort working ...

architecture, frontend/backend patterns, and platform onboarding Mentoring engineers and driving best practices across teams Integrating modern AI tooling (LLMs, vector databases, RAG workflows) into internal platforms What we’re looking for 6+ years experience as a software or platform engineer in complex environments Strong Python engineering background (compiled ...