Solution Architect - LangGraph & Agentic AI

Belmont Lavan Ltd
Full time
🇫🇷Francescas, France
Remote
Visa sponsorship
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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.

  • 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

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