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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