Solution Architect - LangGraph & Agentic AI

Belmont Lavan

1d

About The Position

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

• 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.

Nice To Haves

• 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

Responsibilities

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed