Solutions Architect (amsterdam)

LangChain LangChain · Data AI · Amsterdam, Netherlands · Customer Engineering

LangChain is seeking a Solutions Architect to join their Professional Services team in Amsterdam. The role involves designing, deploying, and optimizing production-grade AI infrastructure and agent systems for enterprise customers. Responsibilities include architecting scalable infrastructure, building agent applications, and engaging with customers. This hybrid role requires expertise in software development, infrastructure/platform engineering, and customer-facing skills, with a focus on multi-agent systems and their underlying infrastructure.

What you'd actually do

  1. Design scalable, highly-available infrastructure for AI platform deployments (compute, storage, networking, security), enterprise integration patterns, Infrastructure as Code (Terraform, Helm), multi-region HA/DR strategies, and CI/CD pipelines
  2. Design multi-agent systems using different patterns, implement agent logic using modern frameworks (langchain/langgraph), design comprehensive evaluation frameworks, optimize prompts with A/B testing, and guide deployment/operations
  3. Lead technical maturity assessments, work directly with enterprise customers to understand requirements and present recommendations, and partner with Engagement Managers and Product/Engineering teams

Skills

Required

  • 7+ years of experience in a technical, hands-on customer-facing roles such as Solutions Architect or Forward Deployed Engineer
  • 3+ years of experience designing and deploying production infrastructure on cloud platforms (GCP, AWS, or Azure)
  • Strong Kubernetes experience (GKE, EKS, or AKS) including cluster design, autoscaling, and multi-zone deployments
  • Experience with Infrastructure as Code (Terraform, Helm) and GitOps practices
  • Knowledge of database systems (relational databases, in-memory data stores) including HA, replication, backup strategies, and sizing
  • Experience designing high-availability and disaster recovery solutions
  • Strong understanding of networking, security (SSO/RBAC, TLS, secrets management), and observability (Prometheus, Grafana, Datadog)
  • Experience with CI/CD pipelines for infrastructure and applications
  • 1+ years of experience building production AI/ML applications or agents
  • Strong experience with LLM frameworks (LangChain, LangGraph, or similar) for building agent-based applications
  • Experience with state management patterns (short-term and long-term memory)
  • Experience designing and implementing evaluation frameworks for AI applications
  • Strong prompt engineering skills with experience in optimization and A/B testing
  • Experience with vector stores, RAG patterns, and knowledge organization
  • Experience with tool integration, API design, and error handling patterns
  • Strong Python and/or TypeScript development skills
  • Customer-facing experience with enterprise customers
  • Experience conducting technical assessments or infrastructure audits
  • Strong communication skills with ability to explain technical concepts to diverse audiences

Nice to have

  • former founders

What the JD emphasized

  • production-grade AI infrastructure and agent systems
  • scalable, secure infrastructure deployments
  • reliable, well-evaluated agent applications
  • multi-agent system architecture
  • production infrastructure
  • production AI/ML applications or agents
  • agent-based applications
  • evaluation frameworks for AI applications

Other signals

  • design, deploy, and optimize production-grade AI infrastructure and agent systems
  • architecting scalable, secure infrastructure deployments
  • building reliable, well-evaluated agent applications
  • multi-agent system architecture
  • customer success
  • best practices
  • cutting-edge AI technology
  • enterprise integration patterns
  • CI/CD pipelines
  • multi-agent systems
  • evaluation frameworks
  • prompt engineering
  • deployment/operations
  • technical maturity assessments
  • production infrastructure
  • Kubernetes
  • Infrastructure as Code
  • GitOps
  • database systems
  • high-availability and disaster recovery
  • networking, security, observability
  • AI/ML applications or agents
  • LLM frameworks
  • state management
  • vector stores, RAG patterns
  • tool integration
  • Python and/or TypeScript