26 to 50 of 75 Retrieval-Augmented Generation Jobs in the Thames Valley

/Vertex AI, including fine-tuning, vector search, and low-latency inference. Build end-to-end LLM applications, leveraging RAG (Retrieval-Augmented Generation), agentic workflows, and prompt engineering. Implement robust evaluation frameworks to monitor LLM quality, hallucinations, token usage, and content safety. Develop … Looking For Essential 5+ years' experience in machine learning engineering or applied AI roles. Recent, demonstrable experience with LLMs, Generative AI, and/or RAG-based systems. Strong Python skills using frameworks such as PyTorch, TensorFlow, Hugging Face, or Google GenAI. Experience with vector databases and retrieval-based ...

fine-tune LLM-based applications (chatbots, summarization, document Q&A, report generation, code assistants, etc.). Apply prompt engineering, RAG (Retrieval-Augmented Generation), and context-aware pipelines to ensure accuracy and relevance. Integrate AI models with enterprise systems, APIs, and data stores … ethics, security, and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (Pinecone, Weaviate, FAISS, Chroma) for semantic retrieval use cases. Partner with business stakeholders to identify and shape ...

fine-tune LLM-based applications (chatbots, summarization, document Q&A, report generation, code assistants, etc.). Apply prompt engineering, RAG (Retrieval-Augmented Generation), and context-aware pipelines to ensure accuracy and relevance. Integrate AI models with enterprise systems, APIs, and data stores … ethics, security, and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (Pinecone, Weaviate, FAISS, Chroma) for semantic retrieval use cases. Partner with business stakeholders to identify and shape ...

fine-tune LLM-based applications (chatbots, summarization, document Q&A, report generation, code assistants, etc.). Apply prompt engineering, RAG (Retrieval-Augmented Generation), and context-aware pipelines to ensure accuracy and relevance. Integrate AI models with enterprise systems, APIs, and data stores … ethics, security, and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (Pinecone, Weaviate, FAISS, Chroma) for semantic retrieval use cases. Partner with business stakeholders to identify and shape ...

fine-tune LLM-based applications (chatbots, summarization, document Q&A, report generation, code assistants, etc.). Apply prompt engineering, RAG (Retrieval-Augmented Generation), and context-aware pipelines to ensure accuracy and relevance. Integrate AI models with enterprise systems, APIs, and data stores … ethics, security, and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (Pinecone, Weaviate, FAISS, Chroma) for semantic retrieval use cases. Partner with business stakeholders to identify and shape ...

analyse and summarise technical content into high-accuracy insights and reports • Deliver production-grade AI agent solutions such as intelligent assistants or insight-generation tools • Collaborate with research, engineering and strategy teams to support technical intelligence workflows • Perform any additional duties reasonably required to support project objectives Required … with agent development frameworks such as LangChain, AutoGen, LlamaIndex, etc • Strong proficiency in Prompt Engineering and Context Engineering • Working experience with RAG (Retrieval-Augmented Generation) • Excellent programming skills (Python preferred) • Solid software engineering background with experience delivering production-quality systems Desired Skills • Experience working ...

Architect & Engineer AI Systems: You won't just call an API; you will build robust processing architectures that leverage off-the-shelf LLMs, RAG pipelines, and context engineering to solve hard problems. You will benchmark these systems rigorously to ensure they actually work, not just "feel" smart. Build the Backend ...

Practical experience with AWS (and ideally Terraform, GitLab CI). Experience with agentic systems or LLM-powered automation (e.g. tool-augmented LLMs, RAG workflows, agents for complex workflows). If this sounds like you, reach ...

Practical experience with AWS (and ideally Terraform, GitLab CI). Experience with agentic systems or LLM-powered automation (e.g. tool-augmented LLMs, RAG workflows, agents for complex workflows). If this sounds like you, reach ...

Practical experience with AWS (and ideally Terraform, GitLab CI). Experience with agentic systems or LLM-powered automation (e.g. tool-augmented LLMs, RAG workflows, agents for complex workflows). If this sounds like you, reach ...

datasets and robust data pipelines CI/CD, monitoring, and observability for ML pipelines Strong problem-solving, communication, and collaboration skills Nice-to-have RAG/LLM pipelines experience Terraform, serverless architectures Cloud data stores (DynamoDB, OpenSearch, BigTable) or graph databases Benefits Life cover, enhanced pension plan, private medical insurance ...

strong parent-company backing. Key Responsibilities Design and deploy AI features (intelligent search, procurement agents, predictive maintenance). Build and integrate LLM-driven agents , RAG pipelines, and predictive models. Collaborate with software engineers to embed AI securely into production. Define AI standards , select tools, and shape the technical roadmap. Opportunity ...

tools, and enterprise connectors. Skills: 6–8 years in Software/application engineering, AI engineering, or applied data engineering. Strong experience with LLMs, embeddings, RAG, retrieval stacks, and vector stores. Hands-on experience in multi-agent systems, agent orchestration, or MCP-like tool patterns. Proficiency in Python, including ...

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 ...

sense of ambiguous problem statements. Our client problems can range in a variety of domains. Some examples, but not limited to, are building a RAG leveraging open source or commercial LLMs, building a Multiagent system, leveraging Small Language models that could be locally resident, problems in computer vision, speech, Natural ...

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 ...

both single-step LLM tasks and complex multi-step agentic workflows. As Lead 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 ...

Scala + React/TypeScript * Experience building modern front-end applications Scala + AI * Experience building AI agents using relevant frameworks * Understanding of LLMs, RAG, tool-building, etc. * Ability to design automations and improve processes using AI If you're excited to work in a fast-paced environment where engineers ...

Transformer-based text classification (BERT/DistilBERT/RoBERTa) • Fine-tuning classification heads • Embeddings + cosine similarity search • Supporting LLM-adjacent work (not deep RAG) Tech Python, sklearn, XGBoost/LightGBM, HuggingFace Transformers, BERT-family models, embeddings, cloud-based ML, CI/CD awareness. What They're Looking For • Strong ...

Transformer-based text classification (BERT/DistilBERT/RoBERTa) • Fine-tuning classification heads • Embeddings + cosine similarity search • Supporting LLM-adjacent work (not deep RAG) Tech Python, sklearn, XGBoost/LightGBM, HuggingFace Transformers, BERT-family models, embeddings, cloud-based ML, CI/CD awareness. What They're Looking For • Strong ...

Transformer-based text classification (BERT/DistilBERT/RoBERTa) • Fine-tuning classification heads • Embeddings + cosine similarity search • Supporting LLM-adjacent work (not deep RAG) Tech Python, sklearn, XGBoost/LightGBM, HuggingFace Transformers, BERT-family models, embeddings, cloud-based ML, CI/CD awareness. What They're Looking For • Strong ...

Transformer-based text classification (BERT/DistilBERT/RoBERTa) • Fine-tuning classification heads • Embeddings + cosine similarity search • Supporting LLM-adjacent work (not deep RAG) Tech Python, sklearn, XGBoost/LightGBM, HuggingFace Transformers, BERT-family models, embeddings, cloud-based ML, CI/CD awareness. What They're Looking For • Strong ...

Transformer-based text classification (BERT/DistilBERT/RoBERTa) • Fine-tuning classification heads • Embeddings + cosine similarity search • Supporting LLM-adjacent work (not deep RAG) Tech Python, sklearn, XGBoost/LightGBM, HuggingFace Transformers, BERT-family models, embeddings, cloud-based ML, CI/CD awareness. What They're Looking For • Strong ...

machine learning, and mathematical concepts Proven experience delivering production-grade data science solutions Hands-on experience with modern data science tooling, including LLMs/RAG approaches Advanced SQL skills Experience working in complex, enterprise-scale environments Strong written and verbal communication skills Ability to perform under technical interviews including live ...

Developing and optimizing models for image recognition, object detection, and video analytics. NLP: Building sophisticated systems for sentiment analysis, entity extraction, semantic search, and RAG architectures leveraging LLMs. Generative AI: Exploring and implementing cutting-edge GenAI techniques for content creation, data augmentation, and innovative product features. SME Consulting & Mentorship … with Computer Vision tasks (e.g., CNNs, object detection models like YOLO). NLP: Expert practical experience in NLP techniques, including transformer models, embedding generation, and building complex text-based applications. 3. Leadership & Soft Skills Technical Leadership: Proven track record of leading complex data science projects from research ...