176 to 200 of 501 Retrieval-Augmented Generation Jobs in London

platforms that support AI‐powered applications, machine learning workloads, or agentic systems. Experience with GCP AI services, vector databases, retrieval‐augmented generation (RAG), model operations, or AI governance frameworks. Experience operating in multi‐cloud environments and helping organisations establish consistent platform standards across cloud ...

enterprise, high‐bar engineering environment. Demonstrated experience designing, building, and deploying large language model applications, including prompt engineering, evaluation, retrieval‐augmented generation, and agent orchestration. Familiarity with modern generative AI components such as large language model APIs, vector stores, embeddings, tool and function calling ...

enterprise, high‐bar engineering environment. Demonstrated experience designing, building, and deploying large language model applications, including prompt engineering, evaluation, retrieval‐augmented generation, and agent orchestration. Familiarity with modern generative AI components such as large language model APIs, vector stores, embeddings, tool and function calling ...

assessment, alignment and misalignment analysis, adversarial testing, and red‐teaming Strong LLM and Generative AI engineering fluency, including prompting strategies, retrieval‐augmented generation, context and memory design, function and tool calling, guardrails, observability, and failure‐mode analysis Track record of taking advanced AI systems ...

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 ...

Migration of analytical/data science workloads (convert and migrate R development/Libraries to Databricks). AI/ML, LLM integration, model lifecycle, RAG/vector retrieval or model-serving experience. Large-scale enterprise platform transformation and regulated-industry experience. Strong cost optimisation and FinOps delivery experience ...

Spark, Iceberg, Kafka, dbt, Scikit-Learn, XGBoost, PyTorch, MLFlow, GraphFrames, Ray, AWS S3, EMR, SageMaker, Lakeformation, Terraform, Docker, GitHub CI/CD, Knowledge Graphs, RAG, graph ML, probabilistic programming, A/B testing. Условия No conditions specified #J-18808-Ljbffr ...

mandatory) tailored to complex banking environments. AI-Native Engineering & Agentic Workflows: Pioneer the integration of AI-native architecture patterns-including agentic workflows, autonomous systems, RAG pipelines, and model-driven orchestration-into core banking, payments, and risk operations. Domain Modernization & Composable Banking: Translate complex business requirements across retail banking, corporate banking ...

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 ...

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 ... ...

modern data architectures, including data lakes/warehouses, governance frameworks, and master/metadata management. Hands‐on experience with Generative AI and LLMs, including RAG, embeddings, vector search, GraphRAG, prompt engineering, and agent frameworks (e.g. LangChain, LlamaIndex). Strong stakeholder skills, able to quickly understand complex business domains and communicate ...

Skills: Proven expertise and hands-on delivery of AI/ML in financial services Strong technical depth in GenAI/LLMs, prompt engineering, RAG architecture, fine-tuning, vector databases Knowledge of evaluation metrics for LLMs and safety/guardrail techniques Understanding of supervised learning and data engineering fundamentals Leadership experience ...

Practice eval‐driven development: Build and own the evaluation harnesses that measure accuracy, hallucination, and performance before anything ships. Solve performance at scale: Tackle RAG latency optimization, context‐window management, and intelligent information routing under real production load. Build observability: Ship tooling that gives the team real‐time insight into ...

productive teams Experience leading teams in fast-growing startups or enterprise software companies Knowledge of financial planning, analytics, or business intelligence domains Experience with RAG systems, vector databases, and semantic search Familiarity with model fine-tuning, evaluation, and continuous improvement workflows Background in distributed systems, microservices, or cloud-native architectures ...

acceptance testing and contract-style testing Observability, monitoring and production debugging Security, compliance, auditability or access-control concerns in product development Prompting, agent workflows, RAG systems or AI-assisted internal tooling Building personal products, prototypes, automation scripts or internal tools Join a mission-driven team addressing real pain points ...

Evaluation techniques Proven experience working end-to-end on Software solutions and CI/CD Pipelines Proficiency in Python and pydantic validation Experience building RAG systems, Prompt engineering, Data processing Preferred Skills Understanding of optimizing both CPU-bound and GPU-bound workloads. Knowledge of monitoring and observability tools (e.g. Prometheus ...

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 ...

with MLOps concepts and frameworks, such as MLflow, Amazon SageMaker, and feature stores. Familiarity with AI concepts, such as Retrieval‐Augmented Generation (RAG), agentic system architectures, evaluation of LLMs and agents, and Model Context Protocol (MCP). Hands‐on experience with or solid understanding ...

agent system behind our AI doctor: reasoning, planning, tool use, clinical citation, and safe fallback behavior Retrieval & knowledge — vector infrastructure, embedding pipelines, RAG architecture over clinical literature and user health data. You know the difference between retrieval that works and retrieval that a clinician ...

scaleup (roughly 30 to 100 people), not exclusively large corporate environments Hands‐on experience shipping AI or LLM features to production: agentic workflows, RAG, or tool use Strong SQL and solid experience with PostgreSQL or an equivalent relational database A genuine preference for staying hands‐on and coding ...

working closely with engineering, cloud and security teams to define practical controls and secure-by-design patterns. This will include areas such as LLMs, RAG, AI agents, vector databases, prompt injection, data leakage, identity, tool/API integration and AI threat modelling, alongside broader cloud and enterprise security architecture. ...

Product management, AI Strategy and engineering Hands-on experience building and scaling AI or machine learning products in production environments. Experience understanding LLMs RAG Architecture and agentic workflows Excellent experience with Python. Excellent experience working with AI platforms (Claude, Open AI and AI orchestration frameworks) Experience within Financial services. ...

technical BizOps role. Hands-on experience with LLM APIs, agentic frameworks, or workflow automation tools (e.g., Python, Zapier/Make, internal tooling, RAG pipelines). Strong SQL and scripting skills; comfortable moving between spreadsheets, code, and no-code tools as the situation demands. A track record of shipping things that ...

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 ...