Senior AI Engineer

• Architect and lead the implementation of multi-agent systems using Google AI SDKs (Vertex AI Agent Builder), LangGraph, CrewAI, and other emerging orchestration frameworks.
• Design and build stateful, tool-augmented agents capable of advanced reasoning, long-term planning, and autonomous execution.
• Develop and document agent orchestration patterns including planner-executor, supervisor-worker, and hierarchical agent structures.
• Implement sophisticated memory systems (short-term, long-term, and cross-session contextual memory).
• Enable seamless cross-agent communication and multi-modal coordination.
• Lead the delivery of production-grade LLM applications: RAG pipelines, specialised agents, and developer copilots.
• Integrate diverse tools, enterprise APIs, and legacy systems into agentic workflows.
• Design robust system prompts, dynamic routing logic, and AI guardrails using Vertex AI Model Garden or Azure AI Studio.
• Drive optimisation of AI workflows for latency, token cost, and output quality.
• Develop and own reusable AI microservices, agent frameworks, and standardised APIs.
• Contribute to core AI platform capabilities including model routing, centralised observability, and safety filters.
• Define and enforce engineering standards and best practices for AI development across the team.
• Deploy and manage agent-based systems on GCP, Azure, and/or AWS using Docker, Kubernetes (GKE/AKS/EKS), and Cloud Run.
• Implement comprehensive monitoring and observability using Vertex AI Inspector, LangSmith, or Azure Monitor.
• Drive incident response and post-mortems for production AI system failures.
• Act as a technical lead on key AI engineering workstreams, shaping architecture and approach.
• Mentor and support more junior AI engineers through code review, design discussions, and pair programming.
• Collaborate with Principal AI Engineer and cross-functional teams (data, product, delivery) to align AI engineering with business outcomes.
• Stay at the forefront of the rapidly evolving agentic AI landscape and bring new approaches into the team.

• 5-8 years of software engineering experience, with at least 3 years focused on LLM-based or AI systems in production.
• Proven track record building and shipping RAG pipelines, autonomous agents, and multi-step reasoning chains.
• Strong hands-on experience with Google AI SDKs, Vertex AI, and/or Azure AI services.
• Deep proficiency in orchestration stacks: LangGraph, CrewAI, LlamaIndex, Haystack, or comparable frameworks.
• Expert-level Python; strong backend development skills (FastAPI, Go, or Node.js).
• Deep understanding of agent design patterns: planning, reflection, memory, and tool-use.
• Experience integrating complex enterprise APIs and event-driven systems into agentic workflows.
• Proven ability to trace, debug, and improve non-deterministic, multi-step AI reasoning pipelines.
• Strong instinct for building resilient, observable, and production-ready AI systems.
• Strong familiarity with GCP and/or Azure core services: GKE, Cloud Run, Azure AI services.
• Infrastructure as Code experience: Terraform or Pulumi.
• Experience building automated evaluation and deployment pipelines for AI models (CI/CD).
• Vector databases (nice to have): Vertex AI Vector Search, Azure AI Search, Pinecone, Weaviate.
• Data pipelines (nice to have): BigQuery, Pub/Sub, Azure Synapse.
• ETL/ELT experience preparing unstructured data for RAG and fine-tuning (nice to have).

Additional Skills & Mindset

• View LLMs as components within a larger system, not just standalone models, and thoughtfully consider architecture, reliability, and cost.
• Take ownership, drive outcomes, and elevate the engineering team.
• Bias toward production-ready, resilient, and observable AI applications.
• Passionate about the evolving landscape of agentic AI and next-generation software architectures.
• Comfortable across cloud platforms and navigating ambiguity in a fast-moving consultancy environment.

• Competitive base salary + performance incentives.
• Health and wellness benefits.
• Flexible hybrid working environment (UK-based).
• Exposure to global clients, cutting-edge AI projects, and a fast-growing AI practice.
• Ongoing learning and development support.