Senior Product Architect

Senior Product Architect – Enterprise Analytics Services

Location: India or United States
Reports To: Director of Engineering – Enterprise Software & Analytics Services (Agile Value Stream Leader)

Position Summary
Qualitrol is building an AI-Native Product Engineering team focused on enterprise analytics services for critical electrical infrastructure. The Product Architect provides technical leadership across multiple product squads, defining architecture while remaining hands-on in specification, software design, implementation, code review, mentoring, and production support. The role guides the evolution of a common enterprise analytics platform deployed from a single code base across cloud-native and customer-managed on-premises environments.

AI-Native Product Engineering
Our engineers begin with precise specifications rather than source code. Using a Specification-Driven Agentic Software Development Lifecycle, Product Engineers collaborate with AI agents to accelerate architecture, implementation, testing, documentation, and validation while retaining full accountability for engineering judgment, software quality, cybersecurity, and customer outcomes.

Mission
Lead the architecture, design, development, and lifecycle evolution of Enterprise Analytics Services while establishing technical direction for reusable platform capabilities that serve multiple analytics domains.

Areas of Stewardship

• Architecture and technical standards

• Product architecture across multiple Agile squads

• Specification-driven engineering practices

• AI-assisted engineering workflows

• Secure, observable, cloud-agnostic enterprise software

• API-first and event-driven architecture

• Technical mentoring and architectural governance

• Production excellence, reliability, and continuous improvement

Key Responsibilities

• Define and evolve the Enterprise Analytics platform architecture.

• Partner with Product Owners to transform end user outcomes into executable engineering specifications.

• Lead architectural reviews, technical design sessions, and implementation strategy.

• Use AI-assisted engineering tools to accelerate production-quality software delivery while maintaining human accountability.

• Establish reusable architectural patterns, APIs, and shared services.

• Drive engineering quality through automated testing, code reviews, and engineering standards.

• Champion secure-by-design, observable, resilient software.

• Mentor Product Engineers and foster technical excellence.

• Collaborate closely with Quality Assurance throughout the SDLC.

• Support SAFe 6.0 Agile planning, architecture runway, and continuous delivery.

What You'll Bring

• Bachelor's or Master's degree in Computer Science, Software Engineering, or related discipline.

• 12+ years building enterprise software with significant architecture leadership.

• Expertise in C#, .NET, C++, ASP.NET Core, Python, cloud-native development, Kubernetes, distributed systems, plugin-based architectures and API-first design.

• Demonstrated success designing software deployable from a common code base across public cloud and customer-managed on-premises environments.

• Strong knowledge of cybersecurity, observability, multi-tenancy, event-driven architecture, and enterprise software engineering.

• Experience mentoring engineers and influencing technical direction across multiple teams.

• Experience with Agile Scrum; SAFe 6.0 preferred.

• Experience with specification-driven development, AI-assisted engineering workflows, or agentic SDLC practices.

Engineering Principles

• Own products, not features.

• Begin with specifications, not code.

• AI accelerates engineering; accountability remains human.

• Architecture is a shared discipline, guided by clear technical leadership.

• Design once. Deploy anywhere.

• Security is a design requirement.

• Observability is a product capability.

• Optimize for customer outcomes.

Success Measures

• Platform architecture adopted across product squads.

• Increased engineering productivity through AI-native engineering practices.

• Improved software quality, resilience, and deployment consistency.

• Reduced technical debt and accelerated release cadence.

• High-performing engineering culture built on ownership and continuous learning.