# Solution Architect - LangGraph & Agentic AI

**Company:** [Belmont Lavan Ltd](null/companies/6heGsPm3cuBbCjvDWApsdc.md)
**Location:** Remote
**Workplace:** remote
**Employment type:** Full-time

[Apply for this job](null/view/35b1810d-4552-4531-a588-47fe1a19e0d8)

## 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