21 of 21 Contract Retrieval-Augmented Generation Jobs in London

hands-on with Python and Azure , possessing a proven track record of designing and deploying Retrieval-Augmented Generation (RAG) patterns. Key Responsibilities Architect & Govern: Design and implement robust on-premises, cloud, and hybrid AI solutions, ensuring strict technical governance and best practices across … client relationships to secure consensus on complex technical architectures. Essential Skills Clearance: Active UK Government Security Check (SC). GenAI: Expertise in LLMs and RAG architectures. Python: Strong proficiency alongside PySpark for data engineering. Azure: Deep familiarity with native tools (AI Search, Document Intelligence). MLOps: Understanding of LLMOps frameworks ...

hands-on with Python and Azure , possessing a proven track record of designing and deploying Retrieval-Augmented Generation (RAG) patterns. Key Responsibilities Architect & Govern: Design and implement robust on-premises, cloud, and hybrid AI solutions, ensuring strict technical governance and best practices across … client relationships to secure consensus on complex technical architectures. Essential Skills Clearance: Active UK Government Security Check (SC). GenAI: Expertise in LLMs and RAG architectures. Python: Strong proficiency alongside PySpark for data engineering. Azure: Deep familiarity with native tools (AI Search, Document Intelligence). MLOps: Understanding of LLMOps frameworks ...

Responsibilities Design & Deliver: Architect and implement cloud and AI-ready solutions on Azure, building repeatable patterns and integration services that support next-generation workloads. Secure AI Architecture: Develop and scale secure infrastructure architectures that allow applications to safely access both cloud-based backend services and on-premises data … Beyond the Basics): Real-world enterprise experience designing and deploying solutions centred around agentic workflows, Retrieval-Augmented Generation (RAG), and vectorisation patterns, rather than purely self-taught or personal interest projects. Software Engineering & Tech Stack: High proficiency in Python and PowerShell for building automation ...

client stakeholders and communicating their business impact. Experience in providing solutions using Agent Harness, Retrieval-Augmented Generation (RAG), GraphRAG, knowledge graphs, code agents, deep research agents, and chatbots. Familiarity with AI engineering platforms (Azure AI Foundry, AWS Bedrock). Knowledge of agentic frameworks (Claude ...

outcomes. The Role and Deliverables Design, build, and maintain production-grade AI-enabled applications consuming standardised GenAI services such as LLM-based capabilities, RAG, summarisation, and classification. Develop cloud-native microservice or full-stack applications using API-led architectures. Integrate AI capabilities securely and reliably, in line with platform standards ...

Architect Professional* Databricks Certifications* CDMP Certification* Terraform, Bicep or Infrastructure-as-Code experience* dbt, Airflow or modern orchestration frameworks* Experience supporting AI, GenAI or RAG-based architectures* Experience within Financial Services, Healthcare, Life Sciences, Energy, Retail or Manufacturing Robert Half Ltd acts as an employment business for temporary positions ...

premises, cloud, and hybrid AI solutions. GenAI Leadership: Deploy and customize a variety of generative models, leveraging Azure OpenAI, Vector Databases, and RAG patterns. Technical Governance: Managed and governed technical teams to ensure best practices in AI data engineering. Stakeholder Management: Act as the primary technical point of contact, gaining ...

deployment and monitoring. Lead end to end delivery of AI and LLM based solutions across underwriting and insurance journeys Build and deploy agentic workflows, RAG pipelines, and LLM powered applications for internal and customer facing products Work across structured and unstructured data, collaborating closely with Machine Learning and GenAI specialists ...

Requirements AWS Bedrock Expert: Hands-on experience with Bedrock Agents, Knowledge Bases, and model lifecycle management. AI/ML Depth: Strong grasp of FMs, RAG, tool-use, and the failure modes of agentic workflows. Security & Compliance: Deep knowledge of NIST AI RMF, OWASP LLM Top 10, and UK/ ...

Requirements AWS Bedrock Expert: Hands-on experience with Bedrock Agents, Knowledge Bases, and model lifecycle management. AI/ML Depth: Strong grasp of FMs, RAG, tool-use, and the failure modes of agentic workflows. Security & Compliance: Deep knowledge of NIST AI RMF, OWASP LLM Top 10, and UK/ ...

Requirements AWS Bedrock Expert: Hands-on experience with Bedrock Agents, Knowledge Bases, and model lifecycle management. AI/ML Depth: Strong grasp of FMs, RAG, tool-use, and the failure modes of agentic workflows. Security & Compliance: Deep knowledge of NIST AI RMF, OWASP LLM Top 10, and UK/ ...

. AWS Bedrock Stack: Hands-on experience with Agents, Knowledge Bases, Guardrails, and model lifecycles. AI/ML Core: Mastery of FMs, RAG, tool-use, and non-deterministic agent logic. Security Frameworks: Proficiency in OWASP LLM Top 10 and NIST AI RMF . Role 1: Identity Expert Mission: Secure ...

application Development: LLM fine-tuning adapting pretrained LLMs for specific tasks using techniques likeparameterefficient fine-tuning (PEFT) (e.g., LoRA, QLoRA). Implementing Retrieval-Augmented Generationpipelines to enhance the knowledge and accuracy of LLMs. Utilizing vector databases for efficient storage andretrieval of embeddings generated by LLMs. rafting ...

for. What you'll be working on as an AI Engineer - LLM & Agentic Systems: Designing and building AI agents and agentic workflows - combining LLMs, RAG, reasoning, memory and tool orchestration to solve real, complex business problems. Owning delivery end-to-end - from initial exploration through to production deployment, integration … with clear, real-world examples you can speak to confidently. Hands-on experience building AI agents including tool use, orchestration and multi-step reasoning - RAG systems that actually deliver, not just proofs of concept. Strong Python skills and solid machine learning foundations - able to write clean, production-grade code with ...

based workflows, integrating AI platforms, and delivering scalable cloud-native solutions. Responsibilities AI Agent Engineering Design and implement AI agents, including: Retrieval (RAG) Orchestration workflows Tool/function invocation Policy-based routing Build evaluation frameworks for accuracy, latency, and reliability Implement observability and monitoring for agent lifecycle … engineering experience Strong experience with cloud-native systems (APIs, microservices, containers, serverless) Experience building and deploying AI/LLM-based systems in production (agents, RAG, orchestration) Proficiency in Python, Java, or similar backend languages Experience with: CI/CD pipelines Infrastructure as code Monitoring and observability tools Hands-on experience ...

enterprise Agentic AI platforms. You will move beyond traditional software QA to build continuous safety pipelines, ensuring our non-deterministic LLM agents, RAG systems, and tool integrations are secure, resilient, and compliant before production release. Key Responsibilities Automated Adversarial Testing: Build and integrate automated red teaming suites into CI/… within FCA, DORA, or EU AI Act frameworks. AWS Bedrock Ecosystem: Hands-on experience configuring, testing, and bypassing Bedrock Guardrails, Agents, and Knowledge Bases (RAG) . AI Security & Fundamentals: Solid understanding of Foundation Models, tool use (function calling), OWASP LLM Top 10 , and NIST AI RMF . Automation Stack: Strong ...

enterprise Agentic AI platforms. You will move beyond traditional software QA to build continuous safety pipelines, ensuring our non-deterministic LLM agents, RAG systems, and tool integrations are secure, resilient, and compliant before production release. Key Responsibilities Automated Adversarial Testing: Build and integrate automated red teaming suites into CI/… within FCA, DORA, or EU AI Act frameworks. AWS Bedrock Ecosystem: Hands-on experience configuring, testing, and bypassing Bedrock Guardrails, Agents, and Knowledge Bases (RAG) . AI Security & Fundamentals: Solid understanding of Foundation Models, tool use (function calling), OWASP LLM Top 10 , and NIST AI RMF . Automation Stack: Strong ...

Development: LLM fine-tuning adapting pretrained LLMs for specific tasks using techniques like parameterefficient fine-tuning (PEFT) (e.g., LoRA, QLoRA). Implementing Retrieval-Augmented Generation pipelines to enhance the knowledge and accuracy of LLMs. Utilizing vector databases for efficient storage and retrieval ...

Financial Services team. You'll be at the intersection of Agentic AI and Zero Trust , building the secure backbone for the next generation of AWS Bedrock deployments. The Baseline FinServ Pro: You know the UK landscape inside out- DORA , FCA Operational Resilience , and the EU AI Act aren … Hands-on experience with AWS Bedrock (Agents, Knowledge Bases, Guardrails, and the full model lifecycle). AI/ML Foundations: Deep understanding of FMs, RAG , non-deterministic agents, and complex tool-use. Secure AI: Expert-level knowledge of OWASP LLM Top 10 and Agentic AI threats . Familiarity with NIST ...

Agents, and Denial-of-Wallet-into rigorous test cases. AWS Bedrock Mastery: Overseeing the secure model lifecycle, including the configuration of Guardrails, Knowledge Bases (RAG), and Agentic tool-use. Regulatory Stewardship: Mapping the firm's safeguard catalogue against the specific demands of DORA, FCA Operational Resilience … appetite. Essential Skill Proven track record in UK Financial Services with deep knowledge of FCA compliance and DORA. Solid grasp of Foundation Models (FMs), RAG, and the risks of non-deterministic agentic behavior. Mastery of OWASP LLM Top 10 and the NIST AI Risk Management Framework. Hands-on experience securing ...

Inside IR35) Scope of work : The consumption layer that determines how agents query and use the graph. RESPONSIBILITIES Defines tool-calling schemas, RAG pipelines, and context strategies. Builds hallucination guardrails and agent evaluation harnesses. Integrates the graph with enterprise agentic platforms. KEY SKILLS LLM integration RAG architecture Tool/function ...