Solution Architect - LangGraph & Agentic AI

Belmont Lavan; Greater London; On-site; GBP 110,000 - 170,000; Full time

Job summary

Belmont Lavan is seeking an experienced Solution Architect to lead the design and deployment of LangGraph-based AI solutions across enterprise platforms. You will translate business needs into secure, scalable architectures and guide engineering teams through production-grade deployments.

The role spans AI architecture, integration, cloud engineering, security, and governance, with a focus on actionable, auditable AI workflows at enterprise scale.

Qualifications

• 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, SLAs, security and constraints into solution architectures.
• Evaluate architectural alternatives and document key decisions and trade-offs.
• Define reusable architecture patterns for agentic AI solutions.

Responsibilities

• Design architectures incorporating: LLMs, LangGraph, RAG, enterprise data, APIs and workflow engines.
• 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 app architectures.
• Collaborate with security, governance, observability, and platform teams.
• Mentor engineers and promote reusable AI architecture patterns.

Skills

LangGraph

LLMs

Agent orchestration

Human-in-the-loop

Security governance

Observability

Python

Cloud architecture

Job description

Requirements

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