AI Architect

Location: London, UK (Hybrid)

Experience: 7+ Years

Employment Type: Contract

We are hiring a highly skilled AI Architect who defines 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 ecosystems, accelerating delivery, enhancing customer experience, strengthening operational resilience, and driving their journey toward a more intelligent, AI-enabled future.

Your responsibilities:

• 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:

• 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:

• 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.