Solution Architect - LangGraph & Agentic AI

We are looking for an experienced Solution Architect with hands-on experience designing and deploying LangGraph-based AI solutions to lead the architecture of enterprise agentic AI platforms and applications. You will work with business and technology stakeholders to identify high-value AI opportunities and translate them into secure, scalable, and production-ready architectures. The role combines AI architecture, enterprise integration, cloud engineering, agentic AI, security, governance, and stakeholder leadership . You will be expected to understand LangGraph at a practical level and be able to challenge architectural decisions, guide engineering teams, and ensure that AI solutions can operate reliably at enterprise scale. Requirements AI Solution Architecture Lead the architecture and design of enterprise AI agent and agentic workflow solutions . Design LangGraph-based architectures for single-agent and multi-agent applications. Translate business requirements, processes, SLAs, security requirements, and technical constraints into solution architectures. Evaluate architectural alternatives and document key technical decisions and trade-offs. Define reusable architecture patterns for agentic AI solutions. Enterprise Agent Architecture Design architectures incorporating: LLMs LangGraph RAG Enterprise data APIs and business systems Workflow engines Human approval processes Observability Security and governance Define appropriate boundaries between AI reasoning and deterministic business logic. Design state management, persistence, recovery, and long-running agent workflows. Determine when to use single-agent, multi-agent, or conventional application architectures. Cloud and Platform Architecture Design scalable AI application architectures on AWS, Azure, or GCP . Define compute, networking, storage, API, security, and platform requirements. Design architectures suitable for enterprise-scale production workloads. Evaluate cloud services and AI platform capabilities based on performance, security, scalability, and cost. Work with platform engineering and DevOps teams to establish deployment standards. Integration Architecture Design integration between AI agents and enterprise applications, APIs, databases, and SaaS platforms. Define secure mechanisms for agent tool access and business-system interactions. Design authentication, authorisation, secrets management, and access-control approaches. Ensure AI-driven actions are traceable, auditable, and appropriately governed. AI Security and Governance Establish security and governance principles for enterprise AI agents. Address risks including: Prompt injection Data leakage Unauthorised tool usage Excessive agent permissions Inaccurate or unsafe actions Sensitive-data exposure Define appropriate human-in-the-loop controls. Ensure solutions comply with organisational security, privacy, regulatory, and responsible-AI requirements. AI Evaluation and Observability Define architecture for AI application m