Solution Architect - LangGraph & Agentic AI

Stellenbeschreibung:

Requirements

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