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Open nowPosted 19 days ago

Solution Architect - LangGraph & Agentic AI

Workable (global search)107,990 open roles

Where
Stuttgart, BW, Germany
Work mode
Remote
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Your applicationOpen nowSolution Architect - LangGraph & Agentic AIWorkable (global search) · Stuttgart, BW, Germany

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7.9% of postings close within 7 days. Measured by our own scanner across the market. Workable (global search) postings stay open a median of 6 days.

Share of postings closed within
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  2. 3.6%3 days
  3. 7.9%7 days
  4. 14.9%14 days
  5. 34.0%30 days
This job: posted 19 days ago

Workable (global search) median: 6 days open

The posting

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
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