76 to 100 of 186 Retrieval-Augmented Generation Jobs in England

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 ...

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 ...

into live products and platforms Work with both commercial and open-source LLMs , selecting the right model for each use case Build and optimise RAG pipelines , embeddings and vector-based retrieval solutions Develop APIs and services that integrate LLMs with existing AI, data and platform systems Optimise solutions … LLMs in production Strong Python development skills Hands-on experience with: Prompt engineering and evaluation Retrieval-Augmented Generation (RAG) Embeddings and vector databases (e.g. FAISS, Pinecone, Weaviate, Chroma) Experience building LLM-backed services using FastAPI/Flask or similar Strong understanding of trade-offs ...

party vendors. Knowledge of OO programming and software design, i.e., SOLID principles. Knowledge and working experience of AGILE methodologies. Familiarity with Databricks, and RAG application architecture a plus Experience with latency optimisation and quantisation preferred Additional Information What’s in it for you?: Competitive salary that reflects your skills, experience ...

Balancing cost, performance, and reliability Applied AI Working with Azure OpenAI APIs beyond prompt demos Retrieval-augmented generation (RAG) Embeddings, vector stores, and evaluation Guardrails, rate limiting, and failure handling Human-in-the-loop workflows Integration & automation Integrating AI into existing enterprise systems Automating ...

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 ...

development is a plus Key responsibilities Design, build and deploy distributed systems and AI agents that leverage LLMs and retrieval‐augmented generation (RAG) to provide contextual, reasoning‐based capabilities. Develop cloud‐native microservices and data pipelines to deliver high‐performance inference and contextual retrieval … software development experience building scalable backend systems; strong knowledge of distributed system architecture, concurrency and microservices. Experience developing LLM‐powered applications, including prompt engineering, RAG and agentic frameworks. Proficiency in one or more programming languages such as Python, Java, Go or Rust; experience with cloud platforms (AWS, Azure ...

experience and API development Experience with data pipelines and vector databases Knowledge of SRE, DevOps, and platform engineering practices Exposure to AI systems, LLMs, RAG, or agent architectures Experience working with UK government or secure environments You'll work alongside expert architects and engineers to deliver complex, high-impact solutions ...

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 ...