Solution Architect - LangGraph & Agentic AI

Belmont Lavan Ltd; United States; Remote; USD 150,000 - 210,000; Full time

Job summary

Belmont Lavan Ltd is seeking an experienced Solution Architect to lead LangGraph-based AI solution architectures across enterprise platforms. You will translate business requirements into secure, scalable, production-ready designs.

The role covers AI architecture, enterprise integration, cloud engineering, security, governance, and observability, with emphasis on LangGraph and ensuring reliable operation at scale.

Qualifications

• Lead the architecture and design of enterprise AI agent and agentic workflow solutions.
• Translate business requirements, processes, SLAs, security requirements, and technical constraints into solution architectures.
• Evaluate architectural alternatives and document key technical decisions and trade-offs.
• Design scalable LangGraph-based architectures for single-agent and multi-agent applications.
• Define reusable architecture patterns for agentic AI solutions.

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 into secure, scalable architectures.
• Evaluate options and document key technical decisions and trade-offs.
• Guide engineering teams and establish deployment standards.

Skills

LangGraph expertise

AI architecture

Enterprise integration

Cloud architecture

Security governance

Stakeholder leadership

Observability

Tools

AWS

Azure

GCP

APIs

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 fo