Senior Manager, SaaS Quality Engineering (AI-First SDLC)

Senior Manager, SaaS Quality Engineering — AI-First SDLC

Location: North America

Organization: PingOne Multi-tenant SaaS

Work model: Hybrid or remote, based on location and business needs

This role is for a deeply technical SaaS engineering leader who has led quality platforms, shift-left quality transformation, and AI-first SDLC transformation at scale. Candidates should be prepared to discuss architecture, quality tools and frameworks, developer-owned quality, and agentic quality workflows.

About the role

Ping Identity’s PingOne Multi-tenant SaaS organization supports mission-critical identity services used by enterprise customers who depend on secure, reliable digital trust. We are looking for a Senior Manager, SaaS Quality Engineering to help define and scale the systems, frameworks, and operating models that improve quality, performance, and AI-first validation across complex multi-tenant SaaS products and microservice platforms. This includes using agentic quality workflows across the SDLC, from requirements and design review through test creation, execution, failure analysis, release readiness, and production feedback loops.

This leader will own North America execution while helping shape global quality strategy across two core pillars: System Validation and Quality Platform. You will partner closely with distributed Quality Engineering, Development, SRE, DevTools, Product, Architecture, Performance Engineering, and AI leaders to define the operating model, lead senior technical talent, and improve how teams build, test, validate, release, and measure production readiness for distributed SaaS systems.

This role is also aligned to the company’s Identity for AI initiative. You will serve as a quality systems leader for PingOne MT’s Identity for AI-aligned work, with a specific focus on testing strategy, validation, evaluation, and release confidence for the PingOne MCP Server and related AI-first product surfaces.

The right candidate has driven production adoption of AI-enabled or agentic engineering workflows in a modern SaaS environment and can explain measurable outcomes, adoption challenges, and lessons learned beyond early pilots. They should also understand how to maintain or improve quality as functional validation shifts from centralized testing teams into developer-owned scrum team workflows.

What you’ll own

Lead AI-first SDLC transformation across the PingOne MT quality organization, including agentic workflows for requirements analysis, test design, test creation, execution, failure triage, coverage analysis, release-readiness evaluation, and governed adoption.

Lead PingOne MT quality strategy and execution across regions, partnering with global leaders and engineers to shape standards, operating model, and measurable delivery outcomes.

Own the quality systems strategy for PingOne MCP Server and Identity for AI-aligned product surfaces, including functional correctness, permissions, tool behavior, auditability, and production readiness.

Lead the Quality Platform pillar: reusable IT, E2E, MCP, and ID4AI test frameworks; define, design, develop and own agentic fleets, quality tooling; CI/CD quality gates; telemetry; and developer-owned quality enablement.

Build the System Validation pillar for customer-oriented E2E validation across products and platforms, while enabling scrum teams to own functional validation closest to their code.

Define validation for critical customer workflows, cross-product scenarios, microservice dependencies, performance and scalability risks, release readiness, escaped-defect prevention, and production-risk reduction.

Partner with DevTools and development leaders to move appropriate validation closer to code through developer-owned unit, integration, and service-level tests.

Lead quality process transformation for AI-first SDLC, quality standards and pipeline-effectiveness forums that turn technical decisions into shared frameworks, trusted CI/CD signals, and clear team ownership.

Use data to improve SDLC effectiveness and production readiness, defect escape rates, test reliability, release confidence, and delivery efficiency.

Hire, coach, and grow senior technical contributors while staying close to architecture, technical reviews, framework direction, and execution.

What success looks like

Success in this role is measured by what the broader engineering organization can do because of your leadership:

a clear validation and evaluation strategy for PingOne MCP Servers and tools,  and Identity for AI-aligned work

measurable adoption of agentic quality process and workflows across the SDLC with appropriate governance

stronger release confidence and higher velocity for critical PingOne customer workflows

better developer-owned quality in a shifted-left model, stronger production readiness, and fewer late-cycle surprises

reusable quality platforms, automation frameworks, and CI/CD signals that engineering teams trust

stronger alignment between Quality Engineering, Development, DevTools, Product, Architecture, and global teams

What we’re looking for

Required

Experience designing, testing, or evaluating AI-agent workflows, MCP servers, agentic quality tools, headless/API-first product experiences, or LLM-backed internal tooling.

Demonstrated success leading AI-first SDLC or agentic quality systems transformation with real organizational adoption, measurable before-and-after impact, and examples you can explain in technical depth.

10+ years of experience in software engineering, quality engineering, engineering productivity, developer platforms, or related technical leadership roles.

5+ years leading engineering or quality engineering teams in a SaaS, cloud, platform, identity, security, developer tooling, or enterprise software environment.

Deep technical experience with multi-tenant SaaS architecture, microservices, distributed systems, and cloud-native platforms.

Strong understanding of scale, tenant isolation, dependency failure modes, blast-radius reduction, resiliency, and production reliability.

Strong track record leading shift-left quality transformation, moving teams from late-cycle validation toward developer-owned unit, integration, service-level, and CI/CD-based quality practices while maintaining or improving release confidence.

Strong experience with CI/CD, UI/API test frameworks, release readiness, quality gates, SLO/SLI-driven validation, production-quality signals, and observability-driven decision making.

Ability to work credibly across Development, SRE, DevTools, Quality Engineering, and Performance Engineering, with a clear understanding of incident response, SLOs/SLIs, production telemetry, capacity, resilience, operational readiness, and each function’s role in a multi-tenant SaaS operating model.

Ability to lead senior engineers through technical judgment, clear ownership, data, and influence while driving execution through roadmaps, KPIs, technical standards, operating cadence, and delivery plans.

Practical experience operating in modern SaaS production environments, including making technical tradeoffs involving CI/CD, feature management, AWS, EKS/Kubernetes, Lambda/serverless, distributed data systems, event-driven systems, and AI/agentic development tools.

Excellent written and verbal communication skills, including async execution discipline across highly distributed global engineering teams and senior stakeholders.

Preferred

Experience with IAM platforms, identity administration, authentication and authorization flows, and multi-tenant identity architectures.

Experience with evaluation frameworks for non-deterministic AI behavior, including deterministic system-state validation, trace review, LLM-as-judge approaches, or human-calibrated scoring.

Ability to guide technical decisions involving test automation, service validation, and performance tooling such as Java, TestNG, Selenium, Playwright, k6, Gatling, JMeter, or similar.

Experience supporting customer-facing multi-tenant platform capabilities where scale, tenant isolation, noisy-neighbor control, security, least privilege, auditability, and operational trust are central requirements.

Experience with observability, incident analysis, and performance platforms such as New Relic, Datadog, Grafana, Prometheus, OpenTelemetry, Splunk, or similar.

Familiarity with distributed data and event technologies such as Cassandra, MongoDB, DynamoDB, Kafka, Redis, SQS/SNS, or similar.

Who thrives here

You’ll likely thrive here if you are deeply technical, outcome-driven, and energized by helping engineering teams move faster with stronger quality signals.

This role is ideal for a leader who can switch between strategy, architecture, execution, people leadership, and technical problem solving — and who has already helped modern SaaS teams change how software gets built, tested, released, and measured in production.

Salary Range: $157,000 - $185,000

In accordance with Colorado’s Equal Pay for Equal Work Act (SB 19-085) the approximate compensation range for this role in Colorado is listed above. Final compensation for this role will be determined by various factors, such as knowledge, skills, and abilities.