# Senior Quality Engineer

**Company:** [Domino](https://hotfix.jobs/companies/domino-data-lab)
**Location:** Remote
**Role:** QA Engineering
**Salary:** $145k – $175k/yr
**Experience:** 5+ years
**Skills:** Kubernetes, AWS, Azure, GCP, CI/CD, RBAC, LLMs, MLOps, Scala, Go, MLflow
**Posted:** 2026-06-04

> Senior Quality Engineer responsible for extending automation frameworks, achieving near-100% test coverage on critical compliance paths, building LLM-assisted testing pipelines, and driving customer defects to zero in a high-velocity MLOps environment. Requires ownership of complex automation platforms, cloud-native expertise, and experience with audit-critical software.

## Job Description

## What your impact will be

In your first year, you will:

- Pick up the existing automation framework, extend it to under-covered surfaces, and harden it for a higher-velocity merge environment
- Push automated coverage on critical paths (policy gates, audit trail, RBAC, lineage, data connectors) toward 100%, and hold the line as merge velocity climbs
- Drive customer-reported defects on compliance-critical surfaces toward zero
- Build an LLM-assisted test pipeline that matches AI-accelerated development with AI-accelerated testing
- Make quality posture visible to leadership through a living risk and coverage map both teams own

## What we look for in this role

### Must-Have Experience

- Owned a complex automation platform: end-to-end frameworks, CI systems, multi-stage automation
- Kubernetes and cloud-native fluency at scale (AWS, Azure, and/or GCP)
- Shipped compliance-critical software where audit trail, RBAC, or governance correctness affected customer audits (pharma, finance, govtech, healthtech, MLOps)
- Senior-level influence across multiple teams without direct authority
- Knowledge of cucu library is a plus

### Bonus Points

- Contract testing for integration-heavy products with connector/plugin matrices
- Stateful workflow / approval-flow testing (state machines, gated transitions, policy engines)
- LLM-augmented QA: test generation, triage, or fuzzing with AI
- MLOps or data-platform domain background (model registries, lineage, MLflow, data lake/warehouse); Scala or Go a plus

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