Make our best customers successful in production and expanding over time.
Improve net revenue retention via adoption, outcomes, and proactive risk management.
Scale your impact to our large user base and OSS community by contributing to documentation, guides, and other public content.
Create a tight loop from “what customers do” (your deep understanding of top customers) → “what we should build” (feedback to the product engineering team) → “how the GTM org explains it.” (GTM enablement).
Own strategic customer relationships (portfolio ownership) — Be the primary technical partner for 10–20 strategic accounts (large, highly engaged, or aligned with our roadmap).
Run onboarding, success planning, and regular deep dives into the customer’s AI architecture and workflows.
Drive adoption of key product capabilities across the lifecycle: initial setup, team workflows, scaling, and expansion.
Lead customers through production readiness: instrumentation strategy, data modeling choices, evaluation setup, alerting/monitoring expectations, security & privacy considerations, and operational playbooks.
Provide pragmatic architecture guidance for real LLM systems (agents, tool use, RAG, evals, prompt iteration, dataset curation, feedback loops).
Build small prototypes, reference implementations, and demos when it unblocks a customer. Turn them into reusable templates that can be published.
Own the technical leadership during high-severity customer moments: triage, root-cause coordination, and crisp communication.
Be the point of contact for the customer and partner closely with Engineering, be proactive in how you resolve issues.
Establish escalation paths, runbooks, and prevention mechanisms for repeat issues.
Aggregate patterns across your portfolio and translate them into actionable product feedback (clear problem statements, impact, and recommended solutions).
Create customer-facing assets (docs, guides, best practices, demos) that start as one customer’s question and become durable collateral.
Enable the broader ClickHouse GTM org: training, playbooks, crisp messaging, and “how to win” narratives for AI engineering teams.

Requirements

Senior experience in a customer-facing technical role: TAM, Solutions Engineer, Solutions Architect, Forward Deployed Engineer, Customer Success Engineer, or similar where you owned outcomes.
Strong technical foundation: you can debug integrations, reason about distributed systems, APIs/SDKs, and cloud infrastructure.
Demonstrated work in applied AI / AI engineering: building, operating, or enabling LLM applications (agents, RAG, eval pipelines, prompt tooling, experimentation).
Excellent communication: you can lead technical meetings, drive decisions, and write docs engineers actually follow.
High ownership: you ship artifacts, close loops, and create repeatable systems rather than bespoke one-offs.
Experience with devtools / OSS ecosystems and developer-centric GTM.
Familiarity with observability concepts (tracing/metrics/logs), data pipelines, and evaluation frameworks.
Track record of technical writing or enablement (workshops, reference architectures, public docs).

Benefits

Offers Equity 📊 Check your resume score for this job Improve your chances of getting an interview by checking your resume score before you apply. Check Resume Score

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

debuggingdistributed systemsAPIsSDKscloud infrastructureapplied AILLM applicationsdata pipelinesobservability conceptstechnical writing

Soft Skills

excellent communicationhigh ownershipleadershipproblem-solvingcollaborationproactive risk managementcustomer relationship managementtrainingdocumentationenablement