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$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
A technical leadership role in LangChain's Professional Services team where you'll architect and deploy production AI infrastructure and agent systems for Fortune 500 customers. Suits experienced engineers who enjoy solving complex infrastructure and AI problems directly with enterprise clients, combining deep technical work with customer-facing strategy.
Our summary, not LangChain’s wording. The full posting is on their site.
Skills this role names
- Amazon Web Services (AWS)
- Datadog
- GitOps
- Grafana
- Helm
- Kubernetes
- LangChain
- LangGraph
- LLMs (Large Language Models)
- Microsoft Azure
- Prometheus
- Python
- Retrieval-Augmented Generation (RAG)
- Terraform
- TypeScript
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What they ask for
Required
- 7+ years in technical, hands-on customer-facing roles like Solutions Architect or Forward Deployed Engineer
- 3+ years designing and deploying production cloud infrastructure on AWS, GCP, or Azure
- Strong Kubernetes experience including cluster design, autoscaling, and multi-zone deployments
- Infrastructure as Code with Terraform and Helm
- Production database systems experience with HA, replication, and backup strategies
- High-availability and disaster recovery architecture design
- Networking and security knowledge including SSO, RBAC, TLS, and secrets management
- Observability tools experience like Prometheus, Grafana, or Datadog
- CI/CD pipeline implementation
- 1+ years building production AI/ML applications or agents
- LLM frameworks experience like LangChain or LangGraph
- AI evaluation frameworks design and implementation
- Prompt engineering and optimization with A/B testing
- Vector stores and RAG pattern experience
- Tool integration and API design for agents
- Python and/or TypeScript development
- Enterprise customer-facing experience
- Technical assessment and infrastructure audit experience
Nice to have
- Former founder background
- State management patterns for short-term and long-term memory
- Experience across multiple LLM frameworks