Solutions Architect (london)

LangChain LangChain · Data AI · London, United Kingdom · Customer Engineering

LangChain is seeking a Solutions Architect to join their Professional Services team in London. The role involves designing, deploying, and optimizing AI infrastructure and agent systems for enterprise customers. Responsibilities include infrastructure engineering (Kubernetes, Terraform, CI/CD), agent engineering (multi-agent systems, evaluation, prompt optimization), and customer engagement. Requires 7+ years of technical customer-facing experience, with specific skills in cloud platforms, Kubernetes, LLM frameworks, and AI application development.

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
  • 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 on cloud platforms
  • production AI/ML applications or agents
  • 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
  • production infrastructure on cloud platforms
  • Kubernetes experience
  • Infrastructure as Code
  • AI applications or agents
  • LLM frameworks
  • evaluation frameworks for AI applications
  • vector stores, RAG patterns
  • tool integration