Solution Architect - LangGraph & Agentic AI

Job description

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