Position: AI Architect Location: London, UK (Hybrid-2 days from office) 6 months contract position The Role In this role, you will define the architectural blueprint and technology foundations for AI driven solutions that transform how customers in dynamic, data intensive industries operate, scale, and innovate. You will design robust, future ready AI architectures that enable automation, advanced analytics, and intelligent decision making across complex digital transformation programmes. With access to cutting edge AI frameworks, high performance computing environments, and modern data platforms, you will guide engineering and data science teams in building secure, scalable, and ethical AI systems. This role empowers you to shape end to end AI ecosystemsaccelerating delivery, enhancing customer experience, strengthening operational resilience, and driving their journey toward a more intelligent, AI enabled future. Your responsibilities: (Up to 10, Avoid repetition) Define the enterprise AI architecture vision and reference patterns; align them to business goals, risk posture, and engineering standards across cloud and hybrid environments. Design secure, scalable AI solutions covering data ingestion, feature engineering, model training, inference, and continuous feedback loops. Establish integration patterns (APIs, events, microservices) to embed model powered capabilities into existing platforms with clear service boundaries. Define enterprise-wide AI architecture guidelines, reusable components, and long-term roadmap to ensure consistency and acceleration of AI initiatives. Implement MLOps/LLMOps pipelines for versioning, CI/CD, approvals, and controlled promotion across environments; enforce reproducibility. Work closely with product owners, data scientists, engineers, security teams, and business stakeholders to ensure architecture translates into high value solutions. Enforce IAM least privilege with IAM Conditions, organisation policies, and scoped service accounts; integrate BeyondCorp for zero trust access. Operationalise observability using Cloud Logging, Cloud Monitoring, Error Reporting, Trace, and Profiler; build model/LLM telemetry dashboards and alerts. Identify the right AI/ML frameworks, cloud services, model orchestration tools, and infrastructure components that align with business needs and scalability goals Architect APIs, microservices, and integration patterns that embed AI capabilities seamlessly into existing workflows and digital products Your Profile Essential skills/knowledge/experience: (Up to 10, Avoid repetition) AI Architect: (Exp Range 7+) Design agentic AI architectures using multi agent orchestration patterns (planner executor, supervisor worker, tool using agents). Define reference architectures for enterprise agent platforms integrating LLMs with systems of record (core banking, CRM, risk, payments). Design audit ready agent interactions, tool usage logs, and decision provenance. Select and standardize frameworks (e.g., LangGraph, Google ADK, MCP, A2A patterns). Hands on expertise with agentic frameworks (orchestrators). Experience with LLMs, prompt engineering, tool/function calling, memory management. API first integration, event driven architectures, and data pipelines. Exposure to AI quality metrics: task success rate, groundedness, containment, FCR. Experience on Google Cloud Platform (preferred) or equivalent hyperscale. Deep understanding of LLMs, generative AI, RAG patterns, vector databases, embeddings, and prompt/guardrail engineering. Desirable skills/knowledge/experience: (As applicable) Knowledge of MLOps / AgentOps, CI/CD, and observability. Strong understanding of regulated financial services environments. Proven experience implementing AI risk controls, model governance, and auditability. Ensure alignment with FCA, PRA, data privacy, model risk management, and LBG internal policies. Knowledge of banking processes: retail banking, lending, payments, compliance. Strong experience designing end to end AI/ML architectures, including data ingestion, feature engineering, model training, deployment, and monitoring. Hands on expertise with cloud AI platforms (GCP). Strong knowledge of LLMOps practices, including CI/CD for models, model registries, feature stores, lineage tracking, and observability. Experience architecting scalable real time, batch, and streaming inference systems with clear performance and reliability requirements. Familiarity with secure cloud patterns, IAM, VPC design, and data protection.

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