Solution Architect – LangGraph, Agentic AI

🔥 1 minute ago

🌐 Netherlands, France, +2 more countries – Remote

🇳🇱 Netherlands – Remote 🇫🇷 France – Remote 🇬🇧 United Kingdom – Remote 🇩🇪 Germany – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

💻 Solutions Engineer

👻 Ghost score 22%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Belmont Lavan

WebsiteLinkedInAll Job Openings

11 - 50 employees

💼 Consulting

📣 Marketing

🎯 Recruiter

Consulting • Marketing • Recruitment

Belmont Lavan is a bespoke Digital Recruitment Agency focused on connecting businesses with top web and digital talent across Europe. The company aims to transform the outdated practices of the recruitment industry by implementing modern methods and technologies. They specialize in various areas of recruitment, including web development, information security, and executive placements. Belmont Lavan emphasizes efficiency for clients by limiting the number of clients they take on, ensuring dedicated attention to each vacancy, and providing candidates with feedback throughout the recruitment process.

📋 Description

• 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 • Design architectures incorporating LLMs, LangGraph, RAG, enterprise data, APIs and business systems, workflow engines, human approval processes, observability, security, and governance • Define 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 • 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 • 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 • Establish security and governance principles for enterprise AI agents • Address prompt injection, data leakage, unauthorised tool usage, excessive agent permissions, inaccurate or unsafe actions, and sensitive-data exposure • Define human-in-the-loop controls • Ensure solutions comply with organisational security, privacy, regulatory, and responsible-AI requirements • Define architecture for AI application monitoring and observability • Establish approaches for evaluating agent accuracy, reliability, latency, cost, and task completion • Define logging, tracing, metrics, and alerting • Establish operational processes for monitoring and continuously improving production agents • 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

🎯 Requirements

• 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: LangChain / LangSmith • Desirable: Multi-agent architectures • Desirable: Enterprise RAG platforms • Desirable: Vector databases • Desirable: Kubernetes • Desirable: Event-driven architectures • Desirable: Microservices • Desirable: Infrastructure as Code • Desirable: CI/CD • Desirable: MLOps / LLMOps • Desirable: AI security • Desirable: Responsible AI • Desirable: Large-scale enterprise transformation • Desirable: Experience working directly with senior client stakeholders