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
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Design architectures incorporating:
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LLMs
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LangGraph
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RAG
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Enterprise data
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APIs and business systems
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Workflow engines
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Human approval processes
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Observability
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Security and governance
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Define appropriate boundaries between AI reasoning and deterministic business logic.
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Design state management, persistence, recovery, and long-running agent workflows.
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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
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Establish security and governance principles for enterprise AI agents.
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Address risks including:
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Prompt injection
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Data leakage
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Unauthorised tool usage
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Excessive agent permissions
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Inaccurate or unsafe actions
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Sensitive-data exposure
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Define appropriate human-in-the-loop controls.
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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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