Solution Architect - LangGraph & Agentic AI in Stuttgart

Requirements

We are looking for an experienced

Solution Architect with hands-on experience designing and deploying Lang Graph-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 Lang Graph 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 Lang Graph-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
• Lang Graph
• 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 Dev Ops teams to establish deployment standards
• Integration Architecture
• Design integration between AI agents and enterprise applications, APIs, databases, and Saa S 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 Lang Graph-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

• Lang Chain / Lang Smith
• 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
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