LLM & RAG Architecture

• Design end-to-end Retrieval-Augmented Generation pipelines including document ingestion, chunking strategy, embedding model selection, and vector store configuration.
• Apply hybrid search (dense + sparse), re-ranking, and query expansion techniques to optimise retrieval quality.
• Model knowledge graph structures to enhance contextual reasoning and relationship traversal across enterprise data.
• Understand and account for LLM behaviour including context window management, temperature tuning, hallucination mitigation, and prompt engineering.

AI Governance and Risk

• Design end-to-end Retrieval-Augmented Generation pipelines including document ingestion, chunking strategy, embedding model selection, and vector store configuration.
• Apply hybrid search (dense + sparse), re-ranking, and query expansion techniques to optimise retrieval quality.
• Model knowledge graph structures to enhance contextual reasoning and relationship traversal across enterprise data.
• Understand and account for LLM behaviour including context window management, temperature tuning, hallucination mitigation, and prompt engineering.

Stakeholder Management

• Act as the primary bridge between business, product, engineering, data science, security, and compliance teams.
• Communicate complex technical architectures to non-technical audiences through clear documentation, diagrams, and presentations.
• Lead architecture reviews, design workshops, and proof-of-concept evaluations with cross-functional teams.
• Provide expert guidance and challenge to senior stakeholders on AI feasibility, risk, and value delivery.

Skills and Experience Required

Legacy Integration

• Proven experience designing integration patterns to ingest and transform data from legacy systems into AI-ready formats.

Platform Integration

• Hands-on experience integrating AI solutions into two or more of: Workday, ServiceNow, SAP, Salesforce, AWS, Azure, Snowflake.

Workflow Transformation

• Demonstrated ability to decompose business processes into autonomous decision chains and design appropriate human oversight mechanisms.

Agentic Frameworks

• Practical experience building production systems with LangGraph and/or CrewAI, including multi-agent coordination and tool orchestration.

LLM & RAG

• Deep knowledge of embeddings, chunking strategies, hybrid search, vector databases, knowledge graphs, and LLM behaviour.

AI Governance

• Experience implementing responsible AI controls, explain-ability mechanisms, and compliance frameworks in production environments.

Stakeholder Management

• Ability to operate across technical and business stakeholders, translating between strategic objectives and engineering delivery.

The strongest candidate will demonstrate the following:

• A portfolio of AI systems delivered into production with clear descriptions of architecture decisions and trade-offs made.
• The ability to walk through a live agentic workflow they designed, explaining how they determined agent boundaries, tool selection, and approval gates.
• Experience of integrating AI into at least one named enterprise platform (e.g. ServiceNow, Salesforce) and the ability to describe the specific integration challenges encountered.
• Confidence discussing AI governance at a practical level, not just policy awareness but actual controls implemented, monitored, and audited.
• Strong written communication: architecture decision records, LLDs, and stakeholder presentations that demonstrate clarity of thought.