1 to 25 of 60 Retrieval-Augmented Generation Jobs in Slough

/ML systems for business intelligence. Build robust APIs, microservices, and data pipelines to power intelligent, data-driven tools. Develop retrieval-augmented generation (RAG) systems using vector databases for contextual AI. Set the technical direction for backend and AI integration best practices. Partner with cross-functional teams to identify and deliver high-value AI … the ability to translate technical outcomes into business impact. Tech Environment Languages: Python, TypeScript, Java AI/LLM: OpenAI, Anthropic, Retrieval-Augmented Generation (RAG) Infrastructure: AWS (Lambda, ECS, S3), Terraform, Docker Databases: PostgreSQL, MySQL, Redis, vector databases DevOps: GitHub, CI/CD pipelines Why Join Competitive salary and comprehensive benefits package 25 days annual More ❯

/ML systems for business intelligence. Build robust APIs, microservices, and data pipelines to power intelligent, data-driven tools. Develop retrieval-augmented generation (RAG) systems using vector databases for contextual AI. Set the technical direction for backend and AI integration best practices. Partner with cross-functional teams to identify and deliver high-value AI … the ability to translate technical outcomes into business impact. Tech Environment Languages: Python, TypeScript, Java AI/LLM: OpenAI, Anthropic, Retrieval-Augmented Generation (RAG) Infrastructure: AWS (Lambda, ECS, S3), Terraform, Docker Databases: PostgreSQL, MySQL, Redis, vector databases DevOps: GitHub, CI/CD pipelines Why Join Competitive salary and comprehensive benefits package 25 days annual More ❯

tune LLM-based applications such as: - Chatbots - Document Q&A systems - Report generators - Code assistants - Summarization tools Apply prompt engineering, Retrieval-Augmented Generation (RAG), and context-aware pipelines to enhance model accuracy and relevance. Integrate AI models with enterprise systems, APIs, and data stores using Python, Java, or Node.js. Collaborate with architects to define … optimization. Ensure compliance with AI 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 (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 … Node.js. Hands-on experience with LLMs (e.g., GPT, LLaMA, Claude, Mistral), Transformers, and Diffusion models. Experience with Hugging Face Transformers, LangChain, LLM orchestration frameworks, and prompt tuning. Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, Vertex AI, Azure More ❯

systems and agentic applications that drive meaningful business outcomes. The Role As our Generative AI Engineer , you’ll be at the forefront of innovation, designing and deploying next-generation AI systems using large language models (LLMs), multi-agent frameworks, and cutting-edge GenAI tooling. You’ll partner closely with stakeholders across Product, Design, and Operations to translate business … AI use cases, translate requirements, and deliver solutions that align with business goals. Develop and fine-tune LLMs and implement Retrieval-Augmented Generation (RAG) pipelines for specialized knowledge tasks. Engineer agentic workflows , including hierarchical agents, swarms, and complex coordination logic for multi-tasking and automation. Integrate GenAI tools into platforms such as WordPress and … proficient in libraries such as NumPy, Pandas, scikit-learn, PyTorch, TensorFlow. Proven experience building and scaling GenAI applications in production. Expertise in LLM architectures (OpenAI, LLaMA, Mistral, etc.) and RAG implementation. Familiarity with frameworks like LangChain, LlamaIndex, LangGraph, CrewAI, AutoGen, or similar. Experience with CI/CD for AI (GenAIOps), model monitoring, and cloud platforms: Google Vertex AI, AWS SageMaker More ❯

about building intelligent, agentic systems using LangGraph. If you thrive at the intersection of LLMs, automation, and complex system design, this is your chance to shape the next generation of AI infrastructure. What You’ll Do Design, implement, and optimize LangGraph-based AI workflows and multi-agent systems Integrate LLMs, APIs, and data pipelines into production-ready solutions … contributions to the open-source project) Strong background in Python, LangChain, OpenAI APIs, and LLM architectures Familiarity with vector databases, retrieval-augmented generation (RAG), and prompt engineering Understanding of software design principles, version control (Git), and CI/CD practices Creative problem-solver with a bias toward action and experimentation Nice to Have Experience More ❯

Model Context Protocol for managing context and tool interfaces for agents. LLM integration patterns, including prompt orchestration and tool calling. Retrieval-Augmented Generation (RAG) for dynamic context injection. Understanding of user-centric design for AI interfaces and intelligent automation. Experience with AI frameworks (PyTorch, Tensorflow, Hugging Face etc.). Preferred Skills Preferred knowledge of More ❯

A leading multi-strategy investment firm is seeking a Senior AI Engineer to join a high-impact Applied AI team focused on building next-generation tools that enhance decision-making across the investment lifecycle. Operating at the intersection of finance and machine intelligence, the team is responsible for developing production-grade AI systems that empower portfolio managers, analysts … containerised environments (Docker, Kubernetes). Solid grasp of distributed systems, networking, and infrastructure-as-code practices. Familiarity with vector databases, retrieval-augmented generation (RAG), and LLM integration is a plus. Exposure to cloud platforms (AWS preferred) and messaging systems (Kafka, RabbitMQ) is advantageous. Strong communication skills and the ability to work across technical and More ❯

root cause analyses. Build and 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 metrics for AI-driven product More ❯

recruiting on behalf of our global energies client for an NLP/GenAI Data Scientist who can bring a strong understanding in modern NLP, LLMs, transformer architectures, prompt-engineering, RAG, agentic architectures and evaluation methodologies. They require candidates to offer strong knowledge of Python programming for developing and debugging AI models and would expect suitable candidates to be educated to … within the GenAI/NLP team. This role will focus on the application and development of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, and domain-specific GenAI solutions to support key internal use cases and products. Responsibilities In this role you will: Design, implement and maintain scalable NLP and GenAI pipelines (including … to date with state-of-the-art research in the space of LLMs/NLP, proposing new ideas and methodologies that unlock business value. Contribute to the development of RAG systems and retrieval pipelines, including chunking, embedding, re-ranking, and evaluation. Participate in experiments, including designing experimental details, writing reusable code, running evaluations, and organising results. Collaborate with More ❯

opportunities and deliver scalable AI workflows. You will design prompts, integrate Large Language Models (LLMs) with enterprise systems, and implement Retrieval-Augmented Generation (RAG) pipelines. You will also advise on responsible AI practices, ensuring compliance and governance. Key responsibilities include: Working closely with Heads of departments to scope out projects Designing and optimizing prompts … for reliable LLM outputs Building and deploying AI-driven workflows connecting LLMs to applications, APIs, and automation tools Implementing RAG pipelines to link enterprise data with LLMs Prototyping and iterating to demonstrate business value quickly Advising on best practices for responsible AI adoption Candidate Profile Strong experience with LLMs (e.g. GPT, Claude, LLaMA) and prompt engineering Hands-on integration with … LlamaIndex, or similar Proficiency in Python or JavaScript for building prototypes and integrations Familiarity with automation platforms (UiPath, Power Automate, Zapier, n8n) Knowledge of vector databases and embeddings for RAG pipelines Excellent communication skills to translate business problems into technical solutions Experience working in agile or fast-paced environments 3+ years experience Location & Working Model Based in London Five days More ❯

in Python and modern AI frameworks (e.g. PyTorch, TensorFlow, LangChain, Hugging Face). Familiarity with LLM fine-tuning, reasoning, or retrieval-augmented generation (RAG). Desirable: Experience with AI orchestration, workflow automation, or public-sector technology projects. Knowledge of frameworks such as ReAct, AutoGen, CrewAI, or BabyAGI. Understanding of ethical AI and governance standards. More ❯

experts across 230 locales, to 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 to market 50% faster. Our mission More ❯

Mistral APIs LLM Frameworks: LangChain, LlamaIndex – for building LLM-powered applications Vector Databases: FAISS, Weaviate, Pinecone, Qdrant (Nice-to-Have) Retrieval-Augmented Generation (RAG): Experience building hybrid systems combining LLMs with enterprise data With a focus within Energy Trading, Oil & Gas, Financial Markets and Commodities, we offer a transparent Recruitment Service that has proven More ❯

experts across 230 locales, to 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 to market 50% faster. Our mission More ❯

LLM, Computer Vision, NLP, Deep Learning Experience with deploying ML models into production An understanding of emerging technologies - such as Retrieval-Augmented Generation (RAG) and Knowledge Graphs A proactive mindset to identify problems and create areas for improvement Degree in Computer Science, AI, Big Data, or equivalent If interested, and the above applies to More ❯

of web content Prototyping algorithms to optimise ad performance and bidding logic Applying modern LLM techniques — from prompt engineering to retrieval-augmented generation (RAG) Working cross-functionally with engineers, product and commercial teams to bring ideas to life What they’re looking for: 1–3 years’ experience in applied ML/AI (or equivalent More ❯

platforms, not research or PoC Deep background in intelligent document processing, OCR, or data extraction pipelines Technical leadership in designing retrieval-augmented generation (RAG) or agentic AI frameworks Experience deploying models in latency-sensitive, compliance-heavy environments Strong engineering foundation, Python, Kubernetes, Rust, vector databases, cloud-native deployment Experience building proprietary products Deep experience More ❯

across multiple industries. What You’ll Be Doing: Designing, developing, and deploying machine learning and AI models Designing, developing, and deploying LLM applications (e.g. GPT, LLaMA, Claude) integrated with RAG pipelines Implementing end-to-end workflows: from data acquisition, cleaning, and feature engineering to model training, deployment, and monitoring Building scalable pipelines and APIs for AI services in cloud environments More ❯

on experience deploying ML models in production environments. Excellent programming skills in Python and familiarity with ML/DL libraries (TensorFlow, PyTorch, scikit-learn, Pandas). Practical experience with RAG or agentic AI frameworks (LangChain, LlamaIndex). Experience working with LLM APIs (e.g. Hugging Face, OpenAI). Exposure to conversational AI platforms (Dialogflow, Lex, Rasa, etc.). Ability to work More ❯

AI/LLM Engineer/Architect (Contract) - RAG … Implementation| London (mostly remote) - Outside IR35 We're looking for a highly skilled AI/LLM Engineer/Architect to lead the implementation of Retrieval Augmented Generation (RAG) solutions for a major client project in London. This is a hands-on contract role for someone who can build from the ground up - shaping the … steer the organisation's AI stack, define best practices, and ensure the delivery of scalable, cost-effective solutions. What You'll Do • Lead the design, build, and deployment of RAG solutions • Drive Agentic development and intelligent automation • Provide guidance on AI architecture and technology strategy • Build and deploy scalable machine learning applications on AWS • Apply ML Ops tooling and workflows More ❯

with productionising and deploying. Strong Python, SQL and programming skills Hands on experience working within areas such as NLP, LLM, recommendation systems Information Retrieval stacks, GenAI (specifically RAG systems). Familiar with architecture and implementation of 1+ ML frameworks (PyTorch, scikit-learn). Experience working in the full model lifecycle (including experimentation, training, testing, monitoring, and deployment). More ❯

experience in AI/ML solutions on Azure, ideally with Generative AI. Expertise with Azure AI, Azure Machine Learning, and Azure OpenAI Service. Strong Python and PyTorch. Experience of RAG pipeline deployment, prompt engineering, fine-tuning foundation models. Knowledge of Responsible AI principles and AI governance. Strong understanding of cloud-native deployment practices. Excellent communication and stakeholder engagement skills. If More ❯

Must-Haves: Technical Chops 5+ years software engineering in production environments Strong in Node.js/TypeScript and Python Experience building microservices architectures Hands-on AI/ML integration (LLMs, RAG, agent frameworks) Cloud infrastructure expertise (GCP/AWS/Azure) Real-time systems and async processing experience Leadership 2+ years leading engineering teams (3-10 people) Proven track record hiring More ❯

with lightweight, framework‐agnostic patterns is valued. Data & infra: SQL, Redis, managed vector DB, S3‐compatible storage, Docket, Sentry. Patterns: function/tool calling, retrieval‐augmented generation (RAG), structured outputs, self‐checks, retries/backoffs, circuit breakers, streaming, KV/cache management. Performance: batching, token/latency/cost monitoring, provider fallbacks, model selection … outputs, context management. Comfortable designing APIs, writing tests, instrumenting systems, and debugging failures across model/provider boundaries. Nice to have Experience with LangChain/LangSmith, Orq.ai, LlamaIndex, DSPy, RAG, Guardrails/Outlines/Instructor. Vector DBs, embeddings, and data pipelines. First 90 days - example outcomes Review and improve existing core Agentic workflows Improve the quality of existing LLM outputs More ❯

decisions. Own operability: set up and maintain CI/CD, monitoring/alerting, performance & error budgets, and safe rollback paths. Build and integrate AI features (LLM/agent workflows, RAG/vector DB integration, eval hooks, cost/safety guardrails). Essential Requirements 3–6+ years building production mobile apps (React Native), with shipped apps. Strong React Native proficiency (Expo More ❯