Solution Architect - LangGraph & Agentic AI

Belmont Lavan Ltd·Francescas, FranceRemotesenior

In short

→Arquitecto de soluciones especializado en AI agente con LangGraph y RAG.
→Diseña arquitecturas escalables, seguras y de producción para flujos de trabajo autónomos en empresas.
→Destacado: liderazgo técnico en arquitectura de IA agente con enfoque en seguridad, integración y gobernanza.

Proficiency in English required for stakeholder collaboration and technical documentation.

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What they ask for

✓Experiencia significativa en arquitectura de soluciones o software.
✓Experiencia práctica con LangGraph y soluciones de IA agente (individual/multiagente).
✓Capacidad para traducir requerimientos empresariales en arquitecturas técnicas.
✓Diseño de arquitecturas seguras con enfoque en gobernanza, auditoría y control humano.
✓Experiencia con integraciones enterprise (APIs, bases de datos, SaaS) y gestión de acceso.
✓Habilidades para liderar equipos técnicos y comunicar decisiones complejas.

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

LangGraphLLMsRAGEnterprise data APIsBusiness systemsWorkflow enginesHuman approval processesObservabilityAWSAzure

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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 .

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