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).
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.
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:
Build Production Grade Agentic AI applications and necessarily have navigated through security, governance and performance management of the end to end model.
Hands‑on expertise on 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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