7 of 7 Contract Retrieval-Augmented Generation Jobs in Central London

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 ...

Foundry , Azure OpenAI , and Azure AI Document Intelligence for enterprise use cases (IDP, GenAI copilots, knowledge retrieval). Establish standards for RAG architectures (vectorization, indexing, chunking, grounding, citation policies) using Azure Search or vector-enabled data stores click apply for full job details ...

Foundry , Azure OpenAI , and Azure AI Document Intelligence for enterprise use cases (IDP, GenAI copilots, knowledge retrieval). Establish standards for RAG architectures (vectorization, indexing, chunking, grounding, citation policies) using Azure Search or vector-enabled data stores. Create architectural decision records (ADRs), guardrails, and reusable blueprints for solution … Analytics, Synapse Analytics, Azure DevOps . You should have hands-on experience in Deep knowledge of LLMs (prompting, system prompts, grounding, evaluation), embeddings , and RAG design (index selection, chunking strategies, reranking, citations). Strong design of secure API and event-driven integration patterns; familiarity with microservices and domain-driven design ...

that enhance global sales workflows. Build scalable backend services and full-stack applications for internal teams. Architect, implement, and optimize LLM orchestration frameworks and RAG-based solutions. Collaborate with cross-functional teams to define requirements and deliver innovative tools. Ensure reliability, scalability, and performance of distributed systems handling massive datasets. … workflows . Strong proficiency in Angular (primary frontend); React optional. Hands-on experience with LangChain, LangGraph, and Indexify , with a deep understanding of RAG architectures . Expert-level SQL and experience with BigQuery and vector databases (Pinecone, Weaviate, Vertex AI Search). Proven track record in MLOps and deploying ...

depth knowledge in implementing solutions using AWS. Deep understanding of AI/ML solutions implementation, with emphasis on LLM applications and retrieval-augmented generation. Practical cloud-native experience, specifically with AWS (compute, storage, networking, IAM, KMS, observability). Experience with RAG-based semantic search ...

LLMs) integrate cleanly into the existing enterprise estate (which currently includes Azure and Snowflake ). Model & Tooling Evaluation: Compare model families, implementation patterns (e.g., RAG patterns ), and platforms (Azure native and broader market options). Provide clear pros and cons to explain trade-offs and recommend the right fit. Integration … designing and implementing end-to-end AI solutions in a commercial environment (not limited to just LLMs). LLM Focus: Solid understanding of RAG patterns, model orchestration, prompt design, and guardrails . Platform Flexibility: Experience evaluating and implementing model families across Azure and non-Azure environments. Broad cloud knowledge across ...

with design systems to create accessible, scalable, reusable UI components Collaborate with designers to test, refine, and elevate user journeys Bring AI capabilities (LLMs, RAG, embeddings) to life in a way that feels natural and user-first Influence product direction by demonstrating what's possible through UX-led prototypes Essential … feature end-to-end , from concept sketches to polished UI Nice to Have Experience with native design patterns (iOS/Android) Familiarity with RAG, embeddings, vector databases Python (for small prototyping tasks) Design system experience (creation or contribution) Experience designing media, content, or large-scale consumer platforms Your ideas could ...