AI Solution Architect

About the role

The AI Solution Architect is a hands‑on technical leader who will design, build, and scale enterprise‑grade AI and agentic AI solutions within our Enterprise AI Center of Excellence. You will translate business challenges into robust technical architectures and ensure solutions are production‑ready, secure, and governed.

Key responsibilities

• Design and deliver end‑to‑end AI and multi‑agent solutions covering data, models, orchestration, integrations, security, deployment, and operations.
• Prototype, develop, test, and harden solutions to meet reliability, scalability, observability, and cost‑control standards.
• Architect multi‑agent systems that can plan, reason, and act through tools, workflows, and enterprise applications.
• Implement guardrails, safety controls, iteration limits, and human‑in‑the‑loop mechanisms for auditable agent behavior.
• Integrate solutions with ERP and other complex platforms using APIs, events, and workflow orchestration.
• Collaborate with platform, security, and application teams to align with enterprise policies.
• Define and maintain AI architecture standards, reference architectures, design patterns, and approved technology stacks.
• Create reusable CoE assets such as templates, checklists, decision trees, evaluation scorecards, and runbooks.

Required profile

• 8‑10 years of experience designing and building enterprise‑scale systems, preferably with ERP or similarly complex platforms.
• Proven hands‑on experience delivering generative AI and multi‑agent solutions for complex business process automation.
• Strong understanding of agent evaluation beyond model accuracy, including task success and reasoning quality.
• Experience with AI Ops/Agent Ops, monitoring behavior, cost, drift, and failure modes in production.

Required skills

• Generative AI and multi‑agent system development
• AI Ops / Agent Ops monitoring
• Enterprise‑scale system architecture
• ERP integration and complex platform connectivity
• Cloud and SaaS architecture (identity, security, networking, deployment)
• API development, integration patterns, workflow orchestration
• CI/CD, containerization, infrastructure as code
• Large Language Models (LLMs), embeddings, Retrieval‑Augmented Generation (RAG), vector search, prompt design, evaluation frameworks