1 to 25 of 60 Retrieval-Augmented Generation Jobs in Central London

Data Version Control Practical experience building LLM/GenAI applications, including prompt engineering and retrieval-augmented generation (RAG) Familiarity with LLMOps frameworks such as LangChain, LangSmith or LangGraph Understanding of model validation, evaluation techniques and production monitoring Experience working in cross-functional teams from ...

unsupervised learning, reinforcement learningand LLMs. Experience with the latest AI engineering approaches such asprompt engineering, retrieval-augmented generation (RAG)and agentic AI. Strong Python skills with a grounding in software engineering best practices (CI/CD, testing, code reviews etc). Expertisein data engineering ...

architecture, engineering, scalability, and deployment. Design and build AI solutions using large language models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI approaches. Remain hands-on with development, contributing to Python code, architecture reviews, and technical problem-solving. Drive best practices across MLOps, testing … development skills with experience across modern machine learning and data science frameworks. Hands-on experience with large language models, generative AI, prompt engineering, and RAG architectures. Experience with Databricks, MLflow, Spark, and cloud-based AI platforms (Azure preferred). Strong understanding of MLOps, software engineering best practices, and production deployment ...

Position Overview We are seeking a Senior Machine Learning Engineer to design, deploy, and optimize our next-generation Conversational AI and Data Analytics platforms. You will bridge the gap between AI research and production engineering. Your primary focus will be optimizing and scaling core Speech … e.g., Kubeflow, MLflow). Preferred Qualifications Conversational Context: Experience with dialogue management, prompt engineering, and Retrieval-Augmented Generation (RAG). Data Analytics: Familiarity with real-time data streaming (e.g., Kafka) and vector databases (e.g., Pinecone, Milvus, Qdrant). Quantization: Experience with model compression techniques ...

learning operations practices Strong working knowledge of large language models (LLMs), including prompting, fine-tuning, retrieval-augmented generation (RAG), and evaluation techniques, and the ability to apply them to real product challenges Experience with agentic AI frameworks and libraries in Python, such ...

reference architectures, POV frameworks, or architectural playbooks — at scale.Fluency across the modern data and AI stack: cloud data platforms (Snowflake, Databricks, BigQuery), agentic frameworks, RAG, vector databases, LLM orchestration, data governance, and enterprise integration patterns.Experience with prompt engineering, agent lifecycle management, and hands-on prototype development.Hands-on proficiency in programming … Redshift.AI & Agentic Architecture Foundational ML concepts, data preparation, Python, applied AI, Generative AI, prompt engineering, retrieval-augmented generation (RAG), vector stores, and agentic architectures. Hands-on familiarity with platforms like Databricks, Snowflake, SageMaker, or Vertex. Agent lifecycle management, observability, and governance frameworks.Agentforce & Salesforce Platform ...

AI. Experience developing AI-enhanced digital assistant solutions such as Microsoft Copilot. Practical experience with Retrieval-Augmented Generation (RAG) architectures and Machine Learning Operations (MLOps). Strong stakeholder engagement skills, with the ability to communicate technical solutions clearly to both technical and non-technical ...

Azure AI Experience building Agentic AI workflows, orchestration frameworks, AI copilots, or autonomous process automation solutions Knowledge of AI-driven APIs, semantic search, RAG architectures, and vector database integrations Azure certifications such as AZ-900, AZ-204, or AI-900 would be advantageous Key Responsibilities Design, develop, and maintain scalable ...

cloud‐native delivery patterns What Will Make You Stand Out Experience building, deploying, and evaluating multi‐agent AI solutions, agent orchestration patterns, RAG architectures, and enterprise integrations Knowledge of agent communication protocols and interoperability patterns, such as MCP and A2A Ability to balance innovation with practical delivery considerations, including risk ...

S3. Strong understanding of MLOps tooling, including MLflow, Weights & Biases, or Data Version Control (DVC). Experience developing LLM or Generative AI applications, including RAG architectures and prompt engineering. Knowledge of LLMOps frameworks such as LangChain, LangGraph, or LangSmith. Experience taking ML models from experimentation into production environments. Strong communication ...

high-quality production code while guiding technical direction Deep hands-on expertise building enterprise AI applications using large language models, AI agents, retrieval-augmented generation, prompt engineering, orchestration frameworks, evaluation methods, and model optimisation techniques Proven ability to take complex AI solutions from prototype … pipelines, and platform capabilities that support real-time and batch AI workloads at enterprise scale Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration Make key technical decisions while remaining accountable ...

implementing end-to-end AI/ML solutions from development through to deployment and monitoring * Building scalable Generative AI and Agentic AI solutions, including RAG pipelines and orchestration frameworks * Implementing MLOps frameworks, governance and best practices * Translating business requirements into cloud-native AI and data architectures across Azure ...

with Python and modern AI/ML frameworks such as PyTorch and/or TensorFlow . Deep understanding of LLMs, Generative AI, prompt engineering, RAG architectures and vector databases . Experience with frameworks and tooling such as LangChain, LangGraph or similar open-source AI toolkits . Experience fine-tuning, evaluating ...

Snowflake, Databricks, BigQuery or equivalent. Strong understanding of Python and SQL environments. Experience delivering Machine Learning models into production. Knowledge of Generative AI, LLMs, RAG and emerging AI technologies. Understanding of MLOps, model deployment, monitoring and lifecycle management. Experience with modern BI and analytics platforms such as Power BI, Tableau ...

using frameworks such as LangGraph and related orchestration tools. Apply modern techniques in:NLPGenerative AIEmbeddings and semantic representationsRetrieval-augmented generation (RAG)AI reasoning and workflow orchestration Evaluate emerging AI models, tools, and frameworks and contribute recommendations for experimentation and adoption. Contribute to prompt engineering, grounding strategies … context management, and hallucination mitigation efforts. Support integration of scientific metadata, ontologies, and knowledge assets into AI-powered workflows. Search, Retrieval & RAG Systems Design, develop, and optimize search and retrieval pipelines , including lexical, vector, and hybrid retrieval approaches. Contribute to the development and enhancement ...

model evaluation, testing, validation, and responsible AI. Design and improve AI systems using techniques such as prompt engineering, retrieval augmented generation (RAG), and AI agents. Mentor data scientists and provide technical leadership through code reviews and coaching. Partner with engineering teams to deliver scalable … What we're looking for Extensive experience developing AI and machine learning solutions in production environments. Strong expertise in large language models, generative AI, RAG, prompt engineering, and agentic AI. Excellent Python programming skills and experience with modern AI and data science frameworks. Strong foundation in statistics, machine learning ...

track record delivering enterprise-scale AI/ML solutions into production Strong experience leading technical teams and complex transformation programmes Deep expertise in LLMs, RAG architectures, Prompt Engineering and Generative AI frameworks such as LangChain or LangGraph Hands-on experience with Python, PyTorch, TensorFlow, MLOps/LLMOps and cloud platforms ...

system design, application development, testing, and operational stability. Experience integrating AI/ML techniques into software systems, including familiarity with LLMs, Generative AI, NLP, RAG, AI evals, and coding assistants. Managing and mentoring software engineering or AI/ML teams, with experience as a hands-on practitioner delivering production-grade ...

architectures, including Kafka Agentic AI and ML Commercial and open‐source LLMs integrated into agentic workflows Tooling for agent orchestration, retrieval‐augmented generation, vector storage, and evaluation Schema validation and structured data handling AI-assisted development Use of AI-assisted and agentic development tools ...

Salesforce Financial Services Cloud | Snowflake | ComplyAdvantage | OCS | SQL | Python | REST APIs | Event-Driven Architecture | Cloud | CI/CD | DevSecOps | AI/ML | GenAI | LLMs | RAG | Data Governance | Data Lineage If you have the above experience please apply immediately ...

repeat manual work. Secure AI Application and Cloud-AI Guardrails Define secure design patterns for AI-enabled applications, including LLM-based features, retrieval-augmented generation, AI agents, AI APIs, copilots, and automation workflows. Establish cloud security requirements for AI workloads, including identity, secrets, data … NIST AI Risk Management Framework, NIST Generative AI Profile, OWASP Top 10 for LLM Applications, or Cloud Security Alliance AI security guidance. Experience with RAG architecture, AI agents, vector databases, AI APIs, prompt engineering, or AI application threat modelling. Experience building security knowledge assistants, prompt libraries, AI-assisted review workflows ...

deliver measurable value. Lead discovery sessions, solution design and technical workshops with senior stakeholders. Design pragmatic AI solutions across Generative AI, Agentic AI, LLMs, RAG and modern cloud platforms. Provide architectural guidance and technical leadership throughout delivery programmes. Remain hands-on where required, supporting engineering teams on complex engagements. Work … before moving into AI. Experience designing enterprise AI solutions using technologies such as: Generative AI Large Language Models (LLMs) Retrieval Augmented Generation (RAG) Agentic AI Cloud AI Services (Azure, AWS or Google Cloud) Comfortable engaging with both technical and non-technical stakeholders. Experience supporting ...

analytical reasoning. Exceptional communication and collaboration skills. Experience with ML frameworks such as TensorFlow, PyTorch, TensorRT, or ONNX. Experience with Large Language Models, including RAG and fine-tuning techniques. Familiarity with compute infrastructure necessary to support operating AI and ML technology. For more information about DRW's processing activities ...

know what it means to balance craft, ownership, and speed Familiarity with tools that power today's AI agents: eval frameworks, agent tooling, RAG pipelines, and prompt engineering Prior experience with React, TypeScript, and/or Go Previous roles where you interfaced with customers or led technical projects with external ...

wider organisation. About the role: This position serves as a subject matter expert for Machine Learning Engineering, supporting production AI/ML, LLM/RAG, and agentic workflow capabilities for legal content products. In addition to writing code on complex systems, this position provides technical direction on architecture, MLOps, responsible … including model lifecycle management, CI/CD, testing, monitoring, release management, observability, and operational support. Practical experience with LLM-based capabilities, including retrieval-augmented generation, semantic search, embeddings, prompt design, evaluation, guardrails, and observability. Experience with agentic workflows, tool orchestration, and multi-step ...