126 to 150 of 277 Retrieval-Augmented Generation Jobs in the UK

integration over pure research). 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 out 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 ❯

Create and maintain technical documentation Communicate complex concepts to all stakeholders. At least 3 years of experience in AI solution engineering. Large Language Models experience including prompt engineering and RAG implementations. Expert data analytics, MLOps practices and API development. Desirable knowledge in Docker and Kubernetes More ❯

growth plans? Do you want to work in a team where innovation is a top priority? In this role you will: Develop and deploy AI-driven applications, integrating LLMs, RAG systems, and multi-agent frameworks. Build scalable data pipelines, APIs, and front-end interfaces to demonstrate real-world impact. Clean, transform, and model large structured and unstructured datasets. Collaborate with More ❯

growth plans? Do you want to work in a team where innovation is a top priority? In this role you will: Develop and deploy AI-driven applications, integrating LLMs, RAG systems, and multi-agent frameworks. Build scalable data pipelines, APIs, and front-end interfaces to demonstrate real-world impact. Clean, transform, and model large structured and unstructured datasets. Collaborate with More ❯

ll Do Define and implement the AI strategy, operating model, and governance structure . Architect and scale AI and ML platforms for enterprise-wide adoption. Lead deployment of LLM, RAG, and Generative AI systems at scale. Partner with business, data, and engineering teams to operationalize AI initiatives. Establish MLOps and LLMOps frameworks for lifecycle automation, compliance, and monitoring. Champion Responsible More ❯

with Google Cloud Run and Kubernetes (GKE) . Familiarity with GCP data tools like BigQuery and Google Cloud Storage (GCS) . Experience building complex agentic AI applications (beyond basic RAG or LangChain implementations). How We Evaluate Candidates Our interview process is designed to identify practical engineering competence and collaborative skills. Technical Acumen: The technical assessment is rigorous. We evaluate More ❯

with Google Cloud Run and Kubernetes (GKE) . Familiarity with GCP data tools like BigQuery and Google Cloud Storage (GCS) . Experience building complex agentic AI applications (beyond basic RAG or LangChain implementations). How We Evaluate Candidates Our interview process is designed to identify practical engineering competence and collaborative skills. Technical Acumen: The technical assessment is rigorous. We evaluate More ❯

Python 3+ years of experience with any of the following fundamental AI technologies: vector search, embedding models, recommender systems, supervised, unsupervised machine learning, deep learning, reinforcement learning, LLM orchestration, RAG systems, etc Familiarity with cloud services and MLOps tooling to deploy and scale data and ML workloads cost-effectively Familiarity with data warehouses (Redshift, BigQuery, Snowflake, etc) and best practices More ❯

support client discussions with compelling demonstrations, and deliver working prototypes as part of engagements. Embed AI capabilities into solutions, including the use of LLMs, Retrieval Augmented Generation (RAG), and embeddings, to create innovative and practical applications. Balance hands on technical delivery with leadership and mentoring responsibilities, guiding a small team of developers and designers. … ideally Azure, with an understanding of how to leverage services for rapid prototyping rather than deep infrastructure expertise. Proven ability to integrate and apply AI technologies such as LLMs, RAG, and embeddings in practical contexts. A mindset that embraces AI as a core part of day to day work, continually seeking opportunities to apply it in design, development, and delivery. More ❯

pipelines to deliver real business impact. Key Skills (Must-Have): Agent-based GenAI app development using Python Prompt engineering and FastAPI AI/ML model development RAG (Retrieval-Augmented Generation) with Vector Databases Strong understanding of Azure Cloud Services , Azure AI Search , and ADI (text, table & image processing) Proficiency with LangGraph and LangChain frameworks More ❯

Studio or custom frameworks). Clear judgment around latency, cost, and reliability trade-offs. Track record of mentoring and raising engineering standards. Nice to have: Retrieval pipelines, RAG evaluation, Kubernetes, CI/CD for ML, and enterprise security exposure. More ❯

Generative AI solutions using Azure AI Foundry, Azure OpenAI and Azure ML. Collaborate with clients to define use cases and translate business needs into technical solutions. Building and implementing RAG pipelines and fine-tuning models. Presenting technical concepts to both technical and non-technical stakeholders. Contributing to agile delivery processes and sharing knowledge through workshops and demos. To be successful More ❯

Generative AI solutions using Azure AI Foundry, Azure OpenAI and Azure ML. Collaborate with clients to define use cases and translate business needs into technical solutions. Building and implementing RAG pipelines and fine-tuning models. Presenting technical concepts to both technical and non-technical stakeholders. Contributing to agile delivery processes and sharing knowledge through workshops and demos. To be successful More ❯

Generative AI solutions using Azure AI Foundry, Azure OpenAI and Azure ML. Collaborate with clients to define use cases and translate business needs into technical solutions. Building and implementing RAG pipelines and fine-tuning models. Presenting technical concepts to both technical and non-technical stakeholders. Contributing to agile delivery processes and sharing knowledge through workshops and demos. To be successful More ❯

Generative AI solutions using Azure AI Foundry, Azure OpenAI and Azure ML. Collaborate with clients to define use cases and translate business needs into technical solutions. Building and implementing RAG pipelines and fine-tuning models. Presenting technical concepts to both technical and non-technical stakeholders. Contributing to agile delivery processes and sharing knowledge through workshops and demos. To be successful More ❯

Generative AI solutions using Azure AI Foundry, Azure OpenAI and Azure ML. Collaborate with clients to define use cases and translate business needs into technical solutions. Building and implementing RAG pipelines and fine-tuning models. Presenting technical concepts to both technical and non-technical stakeholders. Contributing to agile delivery processes and sharing knowledge through workshops and demos. To be successful More ❯

Generative AI solutions using Azure AI Foundry, Azure OpenAI and Azure ML. Collaborate with clients to define use cases and translate business needs into technical solutions. Building and implementing RAG pipelines and fine-tuning models. Presenting technical concepts to both technical and non-technical stakeholders. Contributing to agile delivery processes and sharing knowledge through workshops and demos. To be successful More ❯

Generative AI solutions using Azure AI Foundry, Azure OpenAI and Azure ML. Collaborate with clients to define use cases and translate business needs into technical solutions. Building and implementing RAG pipelines and fine-tuning models. Presenting technical concepts to both technical and non-technical stakeholders. Contributing to agile delivery processes and sharing knowledge through workshops and demos. To be successful More ❯

Generative AI solutions using Azure AI Foundry, Azure OpenAI and Azure ML. Collaborate with clients to define use cases and translate business needs into technical solutions. Building and implementing RAG pipelines and fine-tuning models. Presenting technical concepts to both technical and non-technical stakeholders. Contributing to agile delivery processes and sharing knowledge through workshops and demos. To be successful More ❯

you’ll do: Design and build production-grade AI systems using modern software engineering practices (CI/CD, observability, cloud-native design). Integrate cutting-edge AI technologies — LLMs, RAG, vector databases, AI agents — into scalable enterprise applications. Drive relentless automation and engineering excellence. Partner with data scientists, product managers, and business leaders to turn ideas into high-impact solutions. More ❯

tier enterprise clients. Partner directly with technical and business teams to identify pain points and architect high-impact AI-driven solutions. Build and deploy production-grade systems leveraging LLMs, RAG pipelines, multi-component AI programs (MCPs), and agent frameworks . Conduct applied research to solve novel, previously unseen problems — bringing cutting-edge innovation from concept to production. Contribute to internal More ❯

Qualifications 8-12+ years of experience architecting and delivering data & analytics platforms in enterprise environments. 3+ years of experience with AI/ML and AI enabled analytics (LLMs, RAG, agents). Strong hands on coding skills with Spark (PySpark/Scala), SQL, dbt, and modern data platforms (Databricks, Snowflake, BigQuery). Experience with cloud platforms (AWS preferred). Proven 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 backends/services , with shipped work and clear ownership. Strong Python 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 backends/services , with shipped work and clear ownership. Strong Python More ❯