51 to 75 of 125 Retrieval-Augmented Generation Jobs in the UK

and managing databases in Postgres. Optional Qualifications: Built LLM applications and familiar with the concepts of MCP servers, Agents, and Retrieval-Augmented Generation (RAG) systems. Experience with extract, transform, and load (ETL) data with using Python libraries (e.g. Pandas). Strong understanding of version control systems and CI/CD pipelines to streamline development More ❯

on cutting-edge projects, applying machine learning, NLP, and generative AI to real-world business problems. Key Responsibilities Build and refine AI/ML models, including LLM-based solutions (RAG, fine-tuning, prompt engineering). Design and implement AI-powered applications, integrating with APIs, SQL/NoSQL databases, and Python-based solutions. Prototype and test AI applications, optimizing performance and More ❯

on cutting-edge projects, applying machine learning, NLP, and generative AI to real-world business problems. Key Responsibilities Build and refine AI/ML models, including LLM-based solutions (RAG, fine-tuning, prompt engineering). Design and implement AI-powered applications, integrating with APIs, SQL/NoSQL databases, and Python-based solutions. Prototype and test AI applications, optimizing performance and More ❯

and agentic applications . Are you ready to apply Make sure you understand all the responsibilities and tasks associated with this role before proceeding. You'll work on LLMs, RAG pipelines, model deployment, and cloud-native solutions that drive real-world impact. What you'll bring: Strong coding skills in Java/TypeScript/Python Experience with LLMs, NLP, and More ❯

ll work with cross-functional teams to define strategy, build innovative solutions, and guide stakeholders on AI adoption. Key Responsibilities Build and refine LLM/SLM-based AI solutions (RAG, fine-tuning, prompt engineering). Design and oversee AI solution architecture (cloud & on-prem). Develop and deploy ML models into production. Identify AI opportunities aligned with business processes Prototype More ❯

ll work with cross-functional teams to define strategy, build innovative solutions, and guide stakeholders on AI adoption. Key Responsibilities Build and refine LLM/SLM-based AI solutions (RAG, fine-tuning, prompt engineering). Design and oversee AI solution architecture (cloud & on-prem). Develop and deploy ML models into production. Identify AI opportunities aligned with business processes Prototype More ❯

security practices including OWASP Top 10 and penetration testing . Familiarity with AI/ML systems , including LLM evaluation techniques, output scoring, and validation frameworks. Understanding of prompt engineering , RAG , model orchestration , and hallucination detection . Awareness of ethical AI , bias detection, explainability, and regulatory compliance. Proven ability to work in Agile teams , solve complex problems, and mentor junior team More ❯

e.g. React/Angular and integration with backend. AWS experience or working knowledge of AWS partner solutions like Databricks or Snowflake. Expertise of implementing GenAI in production including LLMs, RAG architectures, vector databases. Experience with automation and scripting (e.g., Terraform, Python). Knowledge of security and compliance standards (e.g., HIPAA, GDPR). Amazon is an equal opportunities employer. We believe More ❯

Own and deliver the roadmap end to end. Drive discovery to iteration, aligning cross functional squads and keeping initiatives on time and aligned to strategy. Integrate and optimize LLM, RAG, and tool calling pipelines for text. Define requirements and run evaluations to optimize our technology stack to text-based agents. Launch new channels into production. Work with clients and deployment More ❯

productionize generative AI models. Develop scalable GenAI pipelines that generate high-quality content, from product descriptions, reviews, titles, and other product content. Design and evaluate prompt tuning strategies and RAG systems to ensure factual and engaging outputs. Fine-tune foundation models and develop domain-specific adapters using techniques like LoRA, PEFT, and instruction tuning. Define best practices for model monitoring More ❯

and ML, whilst planning, developing, and maintaining production. What Youll Bring 3+ years experience in Generative AI experience Proven experience with language-based Generative AI eg. LangChain, LangSmith, LangGraph, RAG, Proficiency in Python, data science libraries such as numpy, Pandas, scikit-learn Experienced in LLM frameworks such as LangChain Ability to build and create prototypes in a fast-paced environment More ❯

incorporating new ideas and techniques into the team. What Youll Bring 3+ years experience in Generative AI experience Proven experience with language-based Generative AI eg. LangChain, LangSmith, LangGraph, RAG, Proficiency in Python, data science libraries such as numpy, Pandas, scikit-learn Experienced in LLM frameworks such as LangChain Strong client-facing consulting or B2B experience Ability to build and More ❯

Architect multi-step agent workflows using: - Semantic Kernel SDK (C# or Python) - Azure OpenAI (GPT-4, function calling, chat completion) - Planner and Kernel Memory APIs for reasoning and memory - RAG pipelines grounded in enterprise data via Azure AI Search Microsoft 365 & Graph API Integration Enable agents to access and reason over content in SharePoint, OneDrive, Teams, Outlook, and Planner. utilise More ❯

Architect multi-step agent workflows using: - Semantic Kernel SDK (C# or Python) - Azure OpenAI (GPT-4, function calling, chat completion) - Planner and Kernel Memory APIs for reasoning and memory - RAG pipelines grounded in enterprise data via Azure AI Search Microsoft 365 & Graph API Integration Enable agents to access and reason over content in SharePoint, OneDrive, Teams, Outlook, and Planner. utilise More ❯

and liaise closely with downstream teams using the platform to build agentic applications. These applications range from consumer-facing chatbots to intelligent backend systems, and use techniques such as RAG and multimodal input/output. Your team has independence and the platform enables some of the most difficult problems at the intersection of our three-sided marketplace (riders, consumers, and More ❯

complex reasoning and workflows. Technical Leadership: Serve as the deep technical expert on the platform for both clients and internal teams. Provide guidance on best practices for data integration, RAG implementation, and agentic automation. Proof of Concept & Prototyping: Lead the technical design and development of Proof of Concept (PoC) projects to demonstrate the feasibility and value of our solutions for … event-driven architecture, and MCP-first design principles. AI/ML Acumen: Strong foundational knowledge of modern AI concepts, including Large Language Models (LLMs), Retrieval Augmented Generation (RAG), and vector databases (e.g., Qdrant, Pinecone, Weaviate). Data Integration: Hands-on experience designing data pipelines and integrating with diverse enterprise data sources (e.g., relational databases More ❯

NLP/ML/Knowledge Graph/GenAI systems for commercial applications Practical experience with traditional and state-of-the-art NLP methods, Knowledge Graph algorithms, and GenAI (including RAG and agentic frameworks) Experience writing production code Outstanding communication, problem-solving, and analysis skills Excellent coding skills in Python Proficiency with Git, AWS, Azure for remote model development and deployment More ❯

across the team to ensure informed decisions are made on the application design and testing approaches. . Contributing to technical decision making alongside colleagues, including Generative AI solutions around RAG and prompt engineering. . Contributing to the required documentation and Agile project maintenance responsibilities. . Helping to design and develop incoming features around sentiment analysis, data ingestion and synthetic audience More ❯

Partnering with stakeholders to scope problems and identify the right solution - whether leveraging existing AI tools or building custom workflows & solutions. Designing and implementing agentic systems using techniques spanning RAG, grounding, prompt engineering, and orchestration on a GCP-first stack. Building and maintaining production ML pipelines and services for non-GenAI use cases (e.g. recommender systems, customer segmentation models, marketing … of MLOps - CI/CD, IaC (Terraform), experiment tracking, model registry, and monitoring. Proven experience deploying and operating ML systems in production (batch and real-time). Familiarity with RAG architectures, prompt engineering, and evaluation techniques. Strong grasp of security, privacy, and governance principles (IAM, secrets, PII handling). Excellent communication skills and ability to work with non-technical stakeholders. More ❯

enterprise-who blends deep technical expertise with sharp delivery instincts. You set the gold standard for safe, scalable AI. Generative AI & Modern Data Platforms Enterprise-grade RAG (Retrieval-Augmented Generation)-ideally on Azure. Agentic AI and M365 Copilot extensibility. Gen AI model strategy, adaptation, and LLMOps. AI safety, governance, and compliance-especially on Azure. More ❯

Nathan Laidlaw from Transparent Tech will be in touch. Software Engineer, Senior Software Engineer, AI Engineer, AI Developer, Python Developer, Full Stack Engineer, Backend Engineer, Generative AI, LLMs, LangChain, RAG, Microservices, CI/CD, GitHub Actions, Cloud Engineering, AWS, GCP, Azure, Vector Databases, Data Pipelines, Applied AI.d02fcfcb-6df4-40ee-95cf-3eb059ce3173 More ❯

activities Oversee quality assurance, code reviews, and unit testing to ensure delivery quality Ensure AI solutions adhere to secure coding, accessibility, and compliance standards Contribute to implementation of LLMs, RAG, Agentic AI, and related frameworks Manage DevOps pipelines and CI/CD practices in Azure Support delivery of cloud-first applications across Azure, Power Platform, and M365 Research and apply More ❯

to junior consultants Supporting Microsoft partnership activities and training sessions Requirements: Proven experience with Microsoft 365 Copilot, Copilot Studio, and related technologies Strong understanding of AI concepts including LLMs, RAG, and prompt engineering Familiarity with Azure services and cloud ecosystems Excellent communication and presentation skills A passion for mentoring and developing engineering talent Experience with distributed systems and incident response More ❯

to junior consultants Supporting Microsoft partnership activities and training sessions Requirements: Proven experience with Microsoft 365 Copilot, Copilot Studio, and related technologies Strong understanding of AI concepts including LLMs, RAG, and prompt engineering Familiarity with Azure services and cloud ecosystems Excellent communication and presentation skills A passion for mentoring and developing engineering talent Experience with distributed systems and incident response More ❯

to junior consultants Supporting Microsoft partnership activities and training sessions Requirements: Proven experience with Microsoft 365 Copilot, Copilot Studio, and related technologies Strong understanding of AI concepts including LLMs, RAG, and prompt engineering Familiarity with Azure services and cloud ecosystems Excellent communication and presentation skills A passion for mentoring and developing engineering talent Experience with distributed systems and incident response More ❯