Location:London, UK (Hybrid)

Experience:7+ Years

Employment Type:Contract

We are Hiring a highly skilledAI Architectwho 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 ecosystemsaccelerating 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

• 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:

• 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