1 to 25 of 65 Remote/Hybrid Retrieval-Augmented Generation Jobs in the UK

have 'under-the-hood' knowledge of Python, specifically for building high-throughput microservices and complex data pipelines using FastAPI, Pandas, and NumPy . The RAG & Agentic Expert: This is the 'Critical' requirement. Our client needs someone with deep hands-on experience building Retrieval-Augmented Generation (RAG) pipelines and Agentic frameworks . You should know how to use LangChain or LlamaIndex to create AI that can execute multi-step tasks. The Data Architect: Proficiency in Vector Databases is essential. You should be comfortable designing data persistence layers using PG Vector, Pinecone, Milvus, or Mongo ...

investment banking, asset management, or capital markets domains.* Deep expertise in ML, NLP, LLMs, retrieval-augmented generation (RAG), embeddings, and modern MLOps practices.* Strong experience working with both structured financial datasets and unstructured data sources (e.g., filings, call transcripts, research).* Familiarity with front ...

just proofs of concept. AI Engineer Responsibilities Own and evolve a production-ready AI platform Design and deliver AI-powered client solutions, including RAG pipelines, agent architectures, LLM integrations, and protocol-driven tooling Build scalable AI systems using modern software engineering best practice (clean code, testing, CI/CD) Develop … experience with Large Language Models (LLMs), including API integrations, prompt engineering, agent workflows, and Retrieval-Augmented Generation (RAG) systems Experience shipping AI-powered features into production environments Proficiency in at least two modern programming languages Familiarity with emerging AI tooling (e.g. MCP, A2A, Threads ...

production. Hands‐on experience with: Python (PyTorch, TensorFlow, scikit‐learn) Data engineering tools (SQL, Spark, Airflow or similar) LLMs, NLP, and document intelligence (e.g. RAG, embeddings) Experience with cloud platforms (AWS, Azure, or GCP). Understanding of MLOps , CI/CD, model monitoring, and governance. M&A/Transaction Experience ...

services Contribute to the development and evolution of a production-grade AI platform Build and integrate AI-powered features such as LLM-driven services, RAG pipelines, and agent-based workflows into core systems Apply modern software engineering best practices (clean code, testing, CI/CD, observability) to all solutions Develop … Desirable: Experience integrating AI into production systems (e.g. chat interfaces, automation, intelligent search) Familiarity with Retrieval-Augmented Generation (RAG), vector databases, or embeddings Experience with cloud platforms (AWS preferred) Awareness of emerging AI tooling (e.g. MCP, A2A, Threads or similar) Open-source contributions ...

features-from data ingestion and feature engineering to model training, evaluation, and deployment Develop LLM/GenAI solutions (prompt engineering, tool use, guardrails) and RAG pipelines (chunking, embeddings, vector search, caching, re ranking) Optimise training and inference performance via batching, quantisation, distillation, LoRA/PEFT, accelerator utilisation (GPU/… building GenAI applications using Python Hands-on Experience with API Development and Microservices architecture and End to End integrations Knowledge of RAG (Retrieval-Augmented Generation) and ADK, MCP Solid understanding of LLMs, prompt engineering, and graph-based workflows Hands-on Experience with API Development ...

About the Role We are seeking a Senior Full-Stack Engineer to join our team and help build the next generation of AI solutions. This is a unique opportunity to be a key technical contributor in a fast-paced, innovative environment where you'll wear many hats … trade-offs Experience with prompt engineering techniques — few-shot prompting, chain-of-thought, structured output, and instruction tuning Practical knowledge of RAG (Retrieval-Augmented Generation) architectures, including chunking strategies, vector embeddings, and similarity search Familiarity with LLM orchestration frameworks such as LangChain, LlamaIndex ...

deploy Generative AI solutions using large language models (LLMs) Develop and optimise prompt engineering strategies for production use cases Build end-to-end pipelines (RAG, embeddings, vector search) for scalable GenAI applications Work with both structured and unstructured datasets, including text-heavy data Collaborate closely with data engineering and software ...

develop GenAI applications for Code Translation, Text Extraction, Summarisation and SDLC Optimization etc. Hands-on Experience with AI Agents, Chat bots, RAG (Retrieval-Augmented Generation), and vector databases (PG vector/croma DB) Hands-on Experience with GenAI Performance Evaluation tools like Pegasus, Ragas ...

Knowledge of MLOps/LLMOps tooling (e.g., MLflow, Weights & Biases, Data Version Control) Experience developing LLM/GenAI applications, including prompt engineering and RAG architectures Ability to absorb complex scientific and technical concepts and apply them to operational solutions Strong communication skills for both technical and non-technical stakeholders Curiosity ...

clients Familiarity with RegTech, compliance, KYB/KYC, or financial services workflows Exposure to search-grounded generation, RAG architectures, or retrieval-augmented workflows Basic programming ability (Python or JavaScript); enough to read code, work with APIs, and write light scripting when needed, even … periphery; you'll be one of the people who shapes how it's configured, extended, and evolved. Real Problems, Real Impact: Search-grounded generation, multi-agent orchestration, structured output reliability, customer-specific workflow customisation; the work is complex, varied, and consequential. What you configure ships and gets used. ...

Weaviate, Chroma) Cloud (AWS/Azure/GCP) Docker, Kubernetes CI/CD, observability, testing for AI systems Experience Strong experience building LLM applications, RAG systems, or AI agents Understanding of prompt engineering, tool-calling, and orchestration frameworks Ability to work autonomously in a scale-up environment A product-focused ...

machine learning lifecycle, model deployment, and MLOps practices Desirable Skills Experience with frameworks such as TensorFlow, PyTorch, or Hugging Face Familiarity with prompt engineering, RAG architectures, and vector databases Knowledge of data governance and security standards within government environments Experience working in agile delivery teams What We Offer Competitive salary ...

requirements Strong background in AI, machine learning or data science Deep experience with LLMs and modern GenAI frameworks Experience designing AI architectures such as RAG and agent-based systems Strong Python and cloud experience across AWS, Azure or GCP Proven track record delivering AI solutions in a client facing environment ...

quality metrics: task success rate, groundedness, containment, FCR Experience on Google Cloud Platform (preferred) or equivalent hyperscale Deep understanding of LLMs, generative AI, RAG patterns, vector databases, embeddings, and prompt/guardrail engineering Desirable Skills/Knowledge/Experience Knowledge of MLOps/AgentOps, CI/CD, and observability Strong ...

modern vector databases. An understanding of Site Reliability Engineering best practices and Platforms/DevOps best practices. Exposure to AI concepts such as LLMs, RAG, and agent‐based systems (or a strong desire to learn). Experience previously working with central government and an understanding of government ways of working ...

problem-solving mindset. Strong Python skills Experience building ML models using frameworks like PyTorch, TensorFlow, or scikit-learn Hands-on experience with LLMs (prompting, RAG, fine-tuning, agents) Understanding of MLOps principles and tools (e.g. MLflow, DVC, Weights & Biases) Experience deploying solutions in AWS or cloud environments Familiarity with Docker ...

problem-solving mindset. Strong Python skills Experience building ML models using frameworks like PyTorch, TensorFlow, or scikit-learn Hands-on experience with LLMs (prompting, RAG, fine-tuning, agents) Understanding of MLOps principles and tools (e.g. MLflow, DVC, Weights & Biases) Experience deploying solutions in AWS or cloud environments Familiarity with Docker ...

problem-solving mindset. Strong Python skills Experience building ML models using frameworks like PyTorch, TensorFlow, or scikit-learn Hands-on experience with LLMs (prompting, RAG, fine-tuning, agents) Understanding of MLOps principles and tools (e.g. MLflow, DVC, Weights & Biases) Experience deploying solutions in AWS or cloud environments Familiarity with Docker ...

problem-solving mindset. Strong Python skills Experience building ML models using frameworks like PyTorch, TensorFlow, or scikit-learn Hands-on experience with LLMs (prompting, RAG, fine-tuning, agents) Understanding of MLOps principles and tools (e.g. MLflow, DVC, Weights & Biases) Experience deploying solutions in AWS or cloud environments Familiarity with Docker ...

systems (LangChain/LangGraph or similar) into production Hands-on experience with AI agents, agentic workflows, and multi-step reasoning Experience designing and implementing RAG systems with real-world impact Familiarity with MCP or similar orchestration patterns (FastMCP/FastAPI) Understanding of LLM limitations and trade-offs (hallucination, latency, cost ...

agentic AI architectures, with experience integrating AI securely and at scale Practical experience in enterprise AI governance, prompt engineering, and deployment approaches (including RAG, fine‐tuning considerations, and risk mitigation) Experience leveraging AI‐assisted development tools (e.g. GitHub Copilot, Claude Code) to materially improve engineering productivity and delivery outcomes ...

agentic AI architectures, with experience integrating AI securely and at scale Practical experience in enterprise AI governance, prompt engineering, and deployment approaches (including RAG, fine-tuning considerations, and risk mitigation) Experience leveraging AI-assisted development tools (e.g. GitHub Copilot, Claude Code) to materially improve engineering productivity and delivery outcomes ...

agentic AI architectures, with experience integrating AI securely and at scale Practical experience in enterprise AI governance, prompt engineering, and deployment approaches (including RAG, fine-tuning considerations, and risk mitigation) Experience leveraging AI-assisted development tools (e.g. GitHub Copilot, Claude Code) to materially improve engineering productivity and delivery outcomes ...

integrating AI/ML features via APIs (e.g. OpenAI, Anthropic or similar) Exposure to data pipelines, inference workflows, or retrieval-based systems (RAG, vector DBs or similar) Cloud & DevOps: AWS, Azure, or GCP Docker, CI/CD pipelines (GitHub Actions, GitLab CI or similar) Experience deploying and maintaining ...