AI Engineer x3 - Active SC - £537 Inside - 3+ Months

• Technical Scoping & Estimation: Break down product requirements and user stories into technical tasks, provide estimates, identify dependencies, risks and integration requirements, and support delivery planning.
• Data Analysis, Engineering & Discovery: Explore and assess structured and unstructured data, identify key data dependencies and quality issues, and prepare, transform and integrate data for use in RAG pipelines, agentic workflows and downstream applications.
• Agentic AI Development: Design and implement multi-step Agentic AI workflows using frameworks such as LangGraph, including workflow state, routing, parallel execution, tool use, human-in-the-loop review, checkpointing and recovery.
• LLM Application Development: Design and develop LLM-powered application capabilities, including prompt engineering, structured outputs, context management, model configuration and validation of model responses.
• RAG & Knowledge Retrieval: Design and implement Retrieval-Augmented Generation solutions, including document ingestion, parsing and chunking, embeddings, indexing, semantic/hybrid search, filtering, reranking and evidence grounding.
• AI Evaluation & Assurance: Develop approaches to evaluate LLM and RAG quality, including grounding, retrieval quality, hallucination, output consistency and regression testing. Implement appropriate guardrails, human review, citations, traceability and auditability.
• Reliability & Error Handling: Design AI workflows to handle rate limits, timeouts, malformed model outputs, partial failures and other non-deterministic behaviours, including retry, recovery and resumability where required.
• Observability & Monitoring: Implement logging, tracing and monitoring across LLM calls, agent workflows and retrieval pipelines to support troubleshooting, performance monitoring, quality assurance and operational support.
• Security & Data Access: Ensure AI solutions follow appropriate authentication, authorisation, data access and secure development practices, particularly when integrating agents with enterprise systems and sensitive information.
• Testing & Peer Review: Develop automated tests for AI and application components, participate in peer reviews and ensure solutions meet agreed quality, security and engineering standards.
• Performance, Cost & Scalability: Consider model selection, token usage, latency, concurrency, retrieval performance and infrastructure cost when designing and optimising AI solutions.
• Technical Documentation: Create and maintain documentation covering solution design, workflows, prompts, dependencies, APIs, operational considerations and support requirements.
• Collaboration & Communication: Work closely with cross-functional teams, including product managers, BA’s, Product Owners and software and platform engineers, to deliver high-quality AI products

The individual must have hands-on experience developing production-grade GenAI/Agentic AI solutions using LLMs, RAG and agentic workflow technologies. The role requires strong Python software engineering skills alongside practical experience designing reliable, secure and maintainable AI-enabled applications., * Strong Python software engineering experience

• Hands-on experience developing GenAI/LLM applications
• Hands-on experience designing and implementing Agentic AI workflows
• Experience with LangGraph
• Strong understanding of LLM workflow design, including state, routing, tools, structured outputs and human-in-the-loop patterns
• Practical experience designing and implementing RAG architectures
• Experience with document ingestion, chunking, embeddings and vector/semantic search
• Experience with prompt engineering and structured LLM outputs
• Experience with AI/LLM evaluation, testing and quality assurance
• Understanding of guardrails, grounding, hallucination mitigation and AI assurance
• Experience building and integrating APIs and Back End services
• Strong understanding of software engineering principles, automated testing, code review and maintainable application design
• Experience handling LLM and agent failure scenarios, retries, error handling and recovery
• Understanding of observability and tracing for GenAI/Agentic AI applications
• Azure AI & Cloud Services: Hands-on experience building and deploying AI solutions on Azure, including Azure OpenAI, Azure AI Search, Azure AI Foundry, Cosmos DB, Redis, Azure Monitor/Application Insights, Managed Identity and Key Vault.