We are seeking a Delivery Engineer to own and evolve the end-to-end engineering feedback loop—connecting development, testing, deployment, and production signals into a continuously improving system.
This role goes beyond traditional QA or DevOps. You will design and operate the systems that determine how safely, quickly, and reliably we deliver software, while ensuring that every production signal feeds back into better engineering decisions.
The ideal candidate is a systems thinker who understands that quality is not a phase—it is an emergent property of a well-designed delivery system.
Responsibilities
Engineering Feedback Loop Ownership
- Design, own, and evolve the end-to-end software delivery system
- Ensure production signals (failures, latency, usage patterns) are continuously fed back into development and testing practices
- Establish systems that improve both speed (velocity) and safety (reliability)
Delivery and Release Strategy
- Design and implement progressive delivery strategies:
- Feature flags
- Canary releases
- Rolling/segmented deployments
- Define and enforce risk-based release practices
- Continuously reduce change failure rate while increasing deployment frequency
CI/CD and Automation
- Own and evolve CI/CD pipelines (GitHub Actions, AWS)
- Build intelligent automation that prioritizes signal quality over volume
- Optimize pipelines for speed, reliability, and developer experience
Quality Engineering
- Build and maintain automated testing systems (Playwright, API, integration)
- Shift quality left (pre-merge validation, static analysis) and right (production validation)
- Ensure tests reflect real-world failure modes, not just happy paths
Observability and Production Intelligence
- Implement and evolve observability systems (New Relic or equivalent)
- Identify failure patterns, regressions, and performance anomalies
- Turn production data into actionable engineering insights, not just dashboards
- Reduce Mean Time to Recovery (MTTR) through improved detection and response systems
Metrics and Continuous Improvement
- Define, track, and improve key delivery metrics:
- Deployment Frequency
- Lead Time for Changes
- Change Failure Rate
- Mean Time to Recovery (MTTR)
- Use metrics to drive system-level improvements, not vanity reporting
AI-Assisted Engineering(Core Responsibility)
- Lead adoption of AI-assisted engineering practices across:
- Test generation
- CI/CD optimization
- Failure triage and debugging
- Improve the signal-to-noise ratio of engineering feedback loops using AI
- Partner with engineering teams to integrate AI into daily workflows
Cross Team Influence
- Define and standardize quality, reliability, and delivery practices across teams
- Partner with QA, SRE, and product engineering to elevate system-wide outcomes
- Act as a force multiplier, improving how all engineers ship software