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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 programs. 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, organization 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.
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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.
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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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