About this role
Enterprise clients deploying large language models often struggle with post-deployment stability, complex system integrations, and timely issue resolution. Unframe needs a technical leader to bridge the gap between Fortune 500 customers, product teams, and core engineering while establishing the company's enterprise support function from scratch.
As the Founding Lead Support Engineer, you will own the entire technical post-deployment customer experience and build the playbooks, SLA frameworks, and diagnostic runbooks needed for global enterprise coverage. You will dive deep into production incidents, manage high-stakes account escalations, and configure autonomous AI tools to automate triage workflows and build self-service knowledge bases.
This position suits an experienced technical problem solver who thrives in early-stage environments and wants to shape support operations alongside AI-first workflows. Success in this role requires a blend of deep diagnostic capabilities, consultative presence with executive stakeholders, and the drive to turn customer friction into actionable product improvements.
What you will do
- Define and build support architecture, SLA frameworks, incident escalation paths, and diagnostic runbooks for global enterprise coverage.
- Bring technical authority and a consultative presence to live video calls with enterprise bank executives and engineering leads.
- Investigate, reproduce, and resolve complex production incidents across web applications, REST APIs, webhooks, microservices, and databases.
- Query SQL databases, parse monitoring logs, trace JSON payloads, and inspect source code in GitHub to isolate root causes before involving core engineering.
- Configure, test, and deploy autonomous AI tools to analyze logs, automate triage workflows, and build self-service knowledge bases.
- Partner closely with product and engineering leadership to turn recurring customer friction and edge cases into actionable product specs and bug fixes.
What you need
- Bring over four years of hands-on experience in technical support engineering, solutions engineering, quality assurance, or site reliability engineering within complex enterprise or B2B SaaS environments.
- Demonstrate the ability to build processes, set technical standards, and mentor colleagues in a fast-paced startup setting.
- Write complex SQL queries to inspect relational and cloud databases.
- Conduct deep log analysis using platforms such as CloudWatch, Datadog, Splunk, Grafana, or Loki.
- Possess a solid understanding of REST APIs, JSON payloads, webhooks, HTTP status codes, and browser developer tools.
- Maintain working familiarity with enterprise authentication protocols including SSO, SAML 2.0, OAuth 2.0, and Okta.
- Utilize generative AI and agentic coding tools daily for log parsing, code navigation, and workflow automation.
- Demonstrate exceptional verbal and written English communication skills with proven experience advising enterprise executives on live technical calls.
Skills and tech stack
Questions candidates ask
What will I actually be working on in this role?
You will establish and lead the enterprise support function while supporting large global enterprise customers using Unframe's AI-powered solutions. You will own the technical post-deployment customer experience, dive deep into system architectures, and build the playbooks, processes, and standards that define customer support as the company scales.
What tools and technologies are used for diagnostics?
The diagnostic tech stack includes writing complex SQL queries for relational and cloud databases, conducting log analysis in CloudWatch, Datadog, Splunk, Grafana, or Loki, and understanding REST APIs, JSON payloads, webhooks, and enterprise authentication protocols like SSO, SAML 2.0, and Okta.
How does the team use AI in the support function?
You will act as an AI Support Orchestrator by configuring, testing, and deploying autonomous AI tools such as Claude Code, Cursor, Copilot, and LLM APIs. These tools are used to analyze logs, automate triage workflows, and build self-service knowledge bases.