Deployed Architect, Professional Services (San Francisco)
LangChainSan Francisco, CA · full time · Senior
$170,000 – $215,000
Listed on LangChain’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
This role combines infrastructure engineering and AI agent development to help enterprise customers build production-grade systems. You'll design cloud deployments, architect multi-agent applications, and guide customers through technical assessments and optimization work.
Our summary, not LangChain’s wording. The full posting is on their site.
Skills this role names
- Amazon Web Services (AWS)
- CI/CD
- Database Design
- Datadog
- Git
- Grafana
- Helm
- Kubernetes
- LangChain
- LangGraph
- Microsoft Azure
- Prometheus
- Python
- Retrieval-Augmented Generation (RAG)
- Role-Based Access Control (RBAC)
- SSL/TLS
- Terraform
- TypeScript
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What they ask for
Required
- 7+ years in customer-facing technical roles such as Solutions Architect or Forward Deployed Engineer
- 3+ years designing and deploying production infrastructure on AWS, GCP, or Azure
- Strong Kubernetes experience including cluster design and multi-zone deployments
- Infrastructure as Code with Terraform and Helm
- Database systems knowledge including HA, replication, and sizing
- High-availability and disaster recovery design
- Networking and security fundamentals (SSO, RBAC, TLS, secrets management)
- Observability tools experience (Prometheus, Grafana, or Datadog)
- CI/CD pipeline experience
- 1+ years building production AI/ML applications or agents
- Experience with LLM frameworks like LangChain or LangGraph
- State management patterns for agents
- Evaluation framework design for AI applications
- Prompt engineering and A/B testing experience
- Vector stores and RAG patterns
- Tool integration and API design
- Enterprise customer experience
- Technical assessment or infrastructure audit experience
Nice to have
- Former founder or unusual background with required skillsets
- Tool integration and error handling patterns
- Experience with multiple LLM frameworks