Sr. Product Manager

The Director Product will be accountable for:

• End-to-end Builder lifecycle governance

• AI enablement roadmap

• Usability and platform discipline

• 24x7 support readiness

• Cross-functional operational alignment

• People leadership in-office

• Own prioritization and roadmap decisions for Builder in alignment with executive leadership.

Own and continuously improve the full lifecycle within Builder:

• Product creation

• Configuration (rates rules forms workflows)

• Versioning & effective-date management

• Validation & testing

• Deployment

• Maintenance

• Product retirement

Establish structured governed enablement processes from creation to shutdown.

Builder must become the authoritative lifecycle platform.

Lead Builders AI evolution including:

• AI Copilot for configuration

• RAG-based assistance

• Agentic workflows

• API tool orchestration

• AI-assisted impact analysis

Define AI roadmap aligned to enterprise SaaS standards.
Ensure AI capabilities are production-grade governed secure and compliant.

The candidate must be able to translate AI capabilities into structured product features and platform workflows.

Builder is used by external customer IT teams and SIs.

Responsibilities include:

• Establish structured support organization

• Implement SLA-driven response model

• Create escalation governance

• Capture and prioritize usability feedback

• Improve customer satisfaction metrics

• Ensure production reliability

• Strengthen DEV / TEST / LIVE separation

• Standardize impact analysis practices

• Reduce configuration defects

• Improve release discipline

• Align with DevOps Cloud ISMS Production Ops

Work closely with:

• Engineering

• QA

• DevOps

• Cloud Infrastructure

• ISMS

• Production Monitoring

• Executive leadership

Balance customer needs scalability security and long-term platform integrity.

• Lead teams in person

• Travel across TN offices

• Coach mentor and elevate BA/config teams

• Raise performance standards

• Build accountability culture

This role requires visible high-energy leadership.

• 5 years product management in SaaS/product company

• Experience owning a platform end-to-end

• Strong people leadership

• Experience with DevOps & production environments

• Experience defining lifecycle governance processes

• Managed enterprise-grade support functions

The candidate must demonstrate strong conceptual understanding of modern AI architectures and how they apply to enterprise SaaS platforms.

This includes familiarity with:

• RAG-based systems (embeddings retrieval grounding concepts)

• AI Copilot and agentic workflow patterns

• Tool/API orchestration models (e.g. MCP-style integration patterns)

• Structured output validation and guardrails

• Context propagation across microservices

• LLM vs traditional ML trade-offs

• Supervised unsupervised and reinforcement learning concepts (at conceptual level)

• Enterprise AI governance compliance and production risk controls

Hands-on model training is not required.

The candidate should have:

• Participated in AI initiatives beyond experimentation

• Contributed to AI-enabled features in a production SaaS environment

• Demonstrated initiative in applying AI to SaaS platforms

Surface-level AI tool usage (e.g. ChatGPT usage alone) is not sufficient.