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.