Artificial Intelligence Architect

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

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.