1 to 25 of 40 Permanent Semantic Search Jobs in England

production—in a modern, AWS-based environment. You'll be part of an engineering and data team tackling projects like LLM development, intelligent automation, semantic search, and recommendation systems. There's real scope here to influence how MLOps and model deployment are done, and you'll be working … Implementing MLOps practices: versioning, experiment tracking, CI/CD, monitoring Optimising for performance, scalability and cost in the cloud Integrating vector databases, embeddings and semantic search Collaborating with engineering, data and product teams What you'll need: Strong Python and experience with TensorFlow, PyTorch or scikit-learn Hands ...

libraries (LangChain, PyTorch, TensorFlow, HuggingFace Transformers). Experience with building Q&A systems and retrieval-augmented generation pipelines. Knowledge of vector databases or semantic search concepts. Familiarity with cloud AI platforms (AWS Bedrock, Azure OpenAI, GCP Vertex AI). Knowledge of MLOps practices and deployment pipelines. Ability … Azure Container Apps (ACA), Azure Kubernetes Service (AKS), or serverless functions (Azure Functions) for event-driven agent triggers. Experience implementing RAG using Azure AI Search (vector, semantic, and hybrid search) and OneLake/Microsoft Fabric. Nice to Have Skills Certification: Microsoft Certified: Azure AI Engineer Associate ...

libraries (LangChain, PyTorch, TensorFlow, HuggingFace Transformers). Experience with building Q&A systems and retrieval-augmented generation pipelines. Knowledge of vector databases or semantic search concepts. Familiarity with cloud AI platforms (AWS Bedrock, Azure OpenAI, GCP Vertex AI). Knowledge of MLOps practices and deployment pipelines. Ability … Azure Container Apps (ACA), Azure Kubernetes Service (AKS), or serverless functions (Azure Functions) for event-driven agent triggers. Experience implementing RAG using Azure AI Search (vector, semantic, and hybrid search) and OneLake/Microsoft Fabric. Nice to Have Skills Certification: Microsoft Certified: Azure AI Engineer Associate ...

that powers our AI-driven capabilities. Rather than reinventing infrastructure, you will leverage managed AI services across GCP and Azure to deliver features like semantic search, retrieval-augmented generation (RAG), and intelligent data enrichment. Working closely with Data and ML Engineers, you will define patterns, select technologies … Evaluate and standardize managed AI services (Vertex AI, Azure AI Foundry, OpenAI/Anthropic APIs) for model hosting, RAG orchestration, and vector search. Vector Search Strategy : Define approach for semantic retrieval using pgvector, AlloyDB AI, or Azure Cognitive Search with embeddings. Cost & Performance : Establish strategies for latency ...

that powers our AI-driven capabilities. Rather than reinventing infrastructure, you will leverage managed AI services across GCP and Azure to deliver features like semantic search, retrieval-augmented generation (RAG), and intelligent data enrichment. Working closely with Data and ML Engineers, you will define patterns, select technologies … Evaluate and standardize managed AI services (Vertex AI, Azure AI Foundry, OpenAI/Anthropic APIs) for model hosting, RAG orchestration, and vector search. Vector Search Strategy : Define approach for semantic retrieval using pgvector, AlloyDB AI, or Azure Cognitive Search with embeddings. Cost & Performance : Establish strategies for latency ...

Design RAG systems: chunking strategies, document schemas, metadata, hybrid/dense retrieval, re-ranking, and grounding. Manage vector/keyword indexes (e.g., Azure AI Search, pgvector, Pinecone/Weaviate). Develop and deploy advanced NLP, information retrieval, and recommendation systems that enhance Chambers and Partners’ research and product offerings … including document understanding, automatic summarisation, topic modelling, semantic search, entity recognition, and relationship extraction. Design and implement intelligent tagging and metadata enrichment frameworks to categorize and organize legal and market data, improving search, discoverability, and insight accuracy. LLM & Machine Learning Application Engineering Design, build, and maintain traditional ...

Research, MLOps, Data Pipelines, Prompt Engineering, Model Fine-Tuning, Cloud Computing, AWS, Azure, Google Cloud, AI Infrastructure, Transformers, Reinforcement Learning, Vector Databases, Pinecone, Weaviate, Semantic Search, API Development, AI Deployment, Model Serving, AI Automation, Early Stage Startup, AI Startups, Tech Startup, Machine Intelligence, Applied AI, AI Applications ...

/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic search and retrieval. Partner with business stakeholders to identify and shape impactful AI use cases. Contribute to the development of a strategic ...

/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic search and retrieval. Partner with business stakeholders to identify and shape impactful AI use cases. Contribute to the development of a strategic ...

distillation of LLMs (e.g., GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. Contributions to open-source AI projects or research publications. Familiarity with AI security, privacy, and compliance standards e.g. ISO42001. ...

distillation of LLMs (e.g., GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. Contributions to open-source AI projects or research publications. Familiarity with AI security, privacy, and compliance standards e.g. ISO42001. ...

language models. Proficiency in Python and familiarity with deep learning/NLP libraries (Langchain, PyTorch, TensorFlow, HuggingFace Transformers). Knowledge of vector databases or semantic search concepts is desirable. Eagerness to learn, strong problem-solving skills, and ability to work collaboratively in agile teams. Experience with Git, collaborative ...

language models. Proficiency in Python and familiarity with deep learning/NLP libraries (Langchain, PyTorch, TensorFlow, HuggingFace Transformers). Knowledge of vector databases or semantic search concepts is desirable. Eagerness to learn, strong problem-solving skills, and ability to work collaboratively in agile teams. Experience with Git, collaborative ...

DTAC) Frontend React TypeScript Tailwind Component-driven development AI/RAG/Knowledge Pipelines LLM-powered features RAG pipelines (embedding models, chunking strategies, semantic search) Document extraction/processing (PDF parsing, OCR, agentic pipelines) Clinical content ingestion workflows Evaluation frameworks for safe medical AI responses … support. Nice to Have Experience coding in TypeScript, JavaScript, Python, or similar. Understanding of cloud systems (AWS preferred). Familiarity with LLMs, embeddings, or semantic search. A portfolio of side projects, tools, or apps you’ve built (clinical or not). Experience in a startup or fast-moving environment. ...

DTAC) Frontend React TypeScript Tailwind Component-driven development AI/RAG/Knowledge Pipelines LLM-powered features RAG pipelines (embedding models, chunking strategies, semantic search) Document extraction/processing (PDF parsing, OCR, agentic pipelines) Clinical content ingestion workflows Evaluation frameworks for safe medical AI responses … support. Nice to Have Experience coding in TypeScript, JavaScript, Python, or similar. Understanding of cloud systems (AWS preferred). Familiarity with LLMs, embeddings, or semantic search. A portfolio of side projects, tools, or apps you’ve built (clinical or not). Experience in a startup or fast-moving environment. ...

Bring • 5+ years backend engineering experience • Strong AWS skills and hands-on cloud build experience • Exposure to LLM/AI workflows, embedding models or semantic search • Startup mindset with a pragmatic, problem-solving approach • Solid grounding in performance, security and scalable architecture Why These Roles Stand ...

Bring • 5+ years backend engineering experience • Strong AWS skills and hands-on cloud build experience • Exposure to LLM/AI workflows, embedding models or semantic search • Startup mindset with a pragmatic, problem-solving approach • Solid grounding in performance, security and scalable architecture Why These Roles Stand ...

experience in production environments. Strong AWS proficiency - hands-on experience building and deploying systems in the cloud. Familiarity with embedding models, chunking strategies, and semantic search. Experience working in a small team or startup environment. A pragmatic problem-solver who balances speed with sustainability. Passion for writing maintainable, scalable ...

experience in production environments. Strong AWS proficiency - hands-on experience building and deploying systems in the cloud. Familiarity with embedding models, chunking strategies, and semantic search. Experience working in a small team or startup environment. A pragmatic problem-solver who balances speed with sustainability. Passion for writing maintainable, scalable ...

experience in production environments. Strong AWS proficiency - hands-on experience building and deploying systems in the cloud. Familiarity with embedding models, chunking strategies, and semantic search. Experience working in a small team or startup environment. A pragmatic problem-solver who balances speed with sustainability. Passion for writing maintainable, scalable ...

experience in production environments. Strong AWS proficiency - hands-on experience building and deploying systems in the cloud. Familiarity with embedding models, chunking strategies, and semantic search. Experience working in a small team or startup environment. A pragmatic problem-solver who balances speed with sustainability. Passion for writing maintainable, scalable ...

experience in production environments. Strong AWS proficiency - hands-on experience building and deploying systems in the cloud. Familiarity with embedding models, chunking strategies, and semantic search. Experience working in a small team or startup environment. A pragmatic problem-solver who balances speed with sustainability. Passion for writing maintainable, scalable ...

experience in production environments. Strong AWS proficiency - hands-on experience building and deploying systems in the cloud. Familiarity with embedding models, chunking strategies, and semantic search. Experience working in a small team or startup environment. A pragmatic problem-solver who balances speed with sustainability. Passion for writing maintainable, scalable ...

demo them internally, gather feedback and iterate Work with huge volumes of unstructured documents and data across multiple legacy systems Use GenAI, LLMs and semantic modelling to extract meaning, entities and patterns Build end-to-end solutions — from extraction to model testing to productionisation Collaborate with business stakeholders, data … real-world ML engineering experience Strong experience with modern GenAI/LLM frameworks: LangChain, LangGraph, PyTorch, etc. Comfort working with unstructured data: entity extraction, semantic search, vectorisation, RAG-like patterns Awareness of data engineering: pipelines, legacy data extraction, interfacing with data teams Experience or strong curiosity working with ...

demo them internally, gather feedback and iterate Work with huge volumes of unstructured documents and data across multiple legacy systems Use GenAI, LLMs and semantic modelling to extract meaning, entities and patterns Build end-to-end solutions — from extraction to model testing to productionisation Collaborate with business stakeholders, data … real-world ML engineering experience Strong experience with modern GenAI/LLM frameworks: LangChain, LangGraph, PyTorch, etc. Comfort working with unstructured data: entity extraction, semantic search, vectorisation, RAG-like patterns Awareness of data engineering: pipelines, legacy data extraction, interfacing with data teams Experience or strong curiosity working with ...