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 monitoring and observability. • Establish approaches for evaluating agent accuracy, reliability, latency, cost, and task completion. • Define appropriate logging, tracing, metrics, and alerting. • Establish operational processes for monitoring and continuously improving production agents. Stakeholder and Technical Leadership • Work directly with senior business and technology stakeholders to define AI strategies and roadmaps. • Lead architecture workshops and technical design sessions. • Communicate complex AI concepts and architectural trade-offs to technical and non-technical audiences. • Provide technical direction to AI engineers, developers, data teams, and platform engineers. • Review solution designs and ensure alignment with enterprise architecture standards. • Mentor engineering teams and promote reusable AI architecture patterns. Required Experience • Significant experience in solution architecture, software architecture, AI architecture, or a related role . • Hands-on experience designing and deploying LangGraph-based AI applications or agentic workflows . • Strong understanding of LLM application architectures. • Experience with enterprise AI/ML solutions in production. • Strong understanding of RAG, tool calling, agent orchestration, and human-in-the-loop patterns. • Strong experience with at least one major cloud platform: AWS, Azure, or GCP . • Strong understanding of enterprise integration patterns and APIs. • Experience with security, governance, observability, and operational requirements for production systems. • Strong technical understanding of Python and modern software engineering practices. Desirable Experience • LangChain / LangSmith • Multi-agent architectures • Enterprise RAG platforms • Vector databases • Kubernetes • Event-driven architectures • Microservices • Infrastructure as Code • CI/CD • MLOps / LLMOps • AI security • Responsible AI • Large-scale enterprise transformation • Experience working directly with senior client stakeholders