Group Product Manager - AI Product Lead - Signals Engineering

Truist•Charlotte, NC

4d•Hybrid

About The Position

The Group Product Manager – AI Signals Engineering owns the product strategy, roadmap, delivery alignment, and stakeholder engagement for the Signals Engineering domain. This role is responsible for translating enterprise signal and telemetry data — spanning system behavior, user interaction, agent execution, and workflow performance — into actionable product capabilities that drive AI quality, safety, and operational intelligence. This is a hybrid product and domain leadership role. The Product Lead sets product vision for signals infrastructure, defines what gets built and why, drives cross-functional delivery alignment, and ensures the observability and intelligence layer needed to operate AI systems with confidence, improve continuously, and meet governance and audit requirements. Daily work includes owning the signals product roadmap, writing product requirements and acceptance criteria, partnering with engineering to deliver telemetry and analytics capabilities, translating enterprise stakeholder needs into product priorities, and ensuring signal outputs are usable, trusted, and integrated into the AI delivery lifecycle.

Requirements

A minimum of fifteen+ years in a combination of professional services and financial services industry
Seven+ years of product management experience
Deep understanding of software development methodologies and best practices
Deep and broad experience in digital banking, financial services, or other complex transactional services
Experience leading complex, cross-functional initiatives and large scale projects
Demonstrable understanding and application of digital concepts and technology
Ability to lead initiatives throughout the software development lifecycle, including post implementation
Bachelors’ degree in business, engineering, design, or technology field; banking or financial management education or equivalent education and related training
Strong strategic thinker, with ability to quickly assess complex problems, prioritize key issues, and focus on relevant facts
Demonstrated experience in managing a varied team of professionals in a project-based environment and a proven ability to coach and develop a team
Outstanding skills presenting/communicating ideas and data to Executive level leaders
Sound business judgment and ability to build a business case around a product or service
Expert relationship builder; developing open, effective, considerate, and productive working relationships. Can “work the matrix” and gain credibility quickly with internal and external constituents
High level of adaptability; responds appropriately and competently to the demands of work challenges when confronted with change, ambiguity, adversity, and other pressures

Nice To Haves

6+ years of product management, product strategy, or technical product ownership experience in enterprise technology, data, analytics, or platform domains.
Demonstrated experience owning product roadmaps, writing requirements, and driving delivery in cross-functional engineering organizations.
Strong understanding of telemetry, observability, behavioral analytics, or signals infrastructure in the context of enterprise software or AI systems.
Experience partnering with engineering teams to translate product requirements into technical specifications, architecture decisions, and delivery plans.
Ability to manage multiple stakeholder groups — engineering, security, governance, business — and synthesize competing priorities into a coherent product direction.
Strong written and verbal communication skills, especially for product briefs, roadmap presentations, and executive-facing delivery updates.
Experience working within enterprise governance, security, and compliance environments where evidence, traceability, and audit readiness matter.
Experience in AI, LLM, or agentic system observability — including prompt telemetry, tool invocation tracking, output scoring, and agent behavior monitoring.
Experience with Microsoft Azure Monitor, Application Insights, Microsoft Fabric, or comparable enterprise telemetry and analytics platforms.
Experience in financial services, cybersecurity, or other regulated enterprise environments with strong data governance, audit, and compliance expectations.
Familiarity with data engineering, pipeline architecture, event streaming, or analytics delivery patterns relevant to signals product ownership.
Experience working at the intersection of product management and AI governance, responsible AI, or enterprise risk management.
Experience defining and tracking product KPIs, signal coverage metrics, and analytics quality benchmarks.

Responsibilities

Own the product vision, strategy, and roadmap for Signals Engineering, defining what telemetry, behavioral analytics, and AI observability capabilities get built, sequenced, and delivered.
Translate enterprise stakeholder needs — from AI engineering, security, governance, QA, and business operations — into clear product requirements, prioritized backlogs, and delivery-ready specifications.
Partner with engineering leads to define signals infrastructure, data capture patterns, telemetry pipelines, event schemas, and analytics delivery that serve AI quality and operational intelligence goals.
Define what "good" looks like for AI signal coverage: what agent behaviors, prompt patterns, tool invocations, workflow outcomes, and system events must be captured, stored, and analyzed.
Drive cross-functional delivery alignment across signals engineering, AI engineering, security, QA, data, and platform teams to ensure the observability layer is production-ready, trusted, and integrated.
Own stakeholder engagement for Signals Engineering — communicating roadmap direction, delivery status, and signal-derived insights to engineering leaders, governance partners, and senior stakeholders.
Establish product metrics and success criteria for signals capabilities, including signal coverage, data latency, analytics usability, and the degree to which signals drive measurable AI quality or risk improvements.
Identify gaps in current signal coverage and prioritize the highest-value observability investments that improve AI safety, reliability, auditability, and continuous improvement velocity.
Partner with AI security and adversarial testing teams to ensure signals infrastructure supports detection, investigation, and remediation of AI-specific risk patterns.
Contribute to the Forge governance and audit readiness posture by ensuring signal data is complete, traceable, retained appropriately, and accessible for compliance and review purposes.