# Head of Operations Engineering

**Company:** [Linktree](https://hotfix.jobs/companies/linktree)
**Location:** Remote
**Role:** ML Engineering
**Experience:** 8+ years
**Skills:** Ml Engineering, Ai Operations, Data Platforms, Llm Tooling, Model Versioning, Prompt Versioning, Evaluation Harnesses, Monitoring, Incident Response, Data Governance, Vendor Risk, Ai Pipelines
**Posted:** 2026-08-27

> Leads the platform, evaluation, governance, economics, and internal enablement required to operate AI systems reliably in production. Requires hands-on ML/AI, data platform, or AI operations experience, strong LLM fluency, and the judgment to guide investment and build-versus-buy decisions.

## Job Description

## Responsibilities
- Own the AI operations platform, including pipelines, model and prompt versioning, evaluation harnesses, monitoring, and rollback.
- Establish standards for evaluating AI features before and after launch.
- Own inference cost, latency, reliability, and related operational metrics.
- Drive internal AI adoption through tooling, agent workflows, and enablement across Product, Engineering, Marketing, and Support.
- Establish proportionate governance for data handling, vendor risk, and pre-launch evaluation.
- Sequence AI investments against the product roadmap and financial model.
- Make build-versus-buy decisions and identify areas the company should not build.
- Mentor engineers and analysts working on AI systems and raise operational standards.

## Requirements
- 5–7 years of experience in ML/AI engineering, data platforms, or AI operations, including hands-on ownership of production systems.
- Experience operating AI or ML systems in production, including pipelines, model and prompt versioning, evaluation, monitoring, and incident response.
- Fluency with modern LLM tooling and the practical constraints of cost, latency, context limits, and nondeterministic outputs.
- Track record of driving internal AI adoption through enablement, tooling, and workflow changes.
- Strong build-versus-buy judgment and comfort making pragmatic decisions with incomplete information.
- Ability to establish practical guardrails for data handling, vendor risk, and pre-launch evaluation.
- Excellent communication skills, including explaining AI trade-offs to engineers and executives.

## Compensation and Benefits
- Competitive compensation package including cash, equity, and benefits.
- Annual wellbeing allowance.
- Employer retirement contribution.
- Generous vacation, holiday, parental, and volunteer leave.
- Employee stock option program.

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