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Remote - Nationwide, United States of America
Remote only
Schellman is a Top 50 CPA firm and a leading provider of attestation and compliance services. Our professional services focus on security and privacy audits, assessments, and certifications. Schellman has become one of the largest cybersecurity assessment firms in the United States without providing any traditional accounting services. We are an accredited multi-framework ISO Certification Body for security, privacy, business continuity, and quality; a globally licensed PCI Qualified Security Assessor and a top provider to clients serving the federal DoD space as a leading FedRAMP 3PAO and the first assessment firm authorized as a CMMC C3PAO. Our specialty and expertise remain in providing best in class Cybersecurity and IT Audits and Attestations. Our culture, approach with clients, and dedication to our values has led us to consistently be a Great Places to Work certified company and rated as a Best Firms to Work For by Accounting Today and a Glassdoor Best Places to Work. We deeply appreciate our employees, as shown by our first core value – People Come First. This is demonstrated in our culture, benefits, and how we handle business. Come see what makes Schellman special!
The Senior AI Product Manager is a hands-on, strategic technical leader responsible for developing and delivering AI-powered automation and tooling across Schellman's internal operations and service delivery teams. This role bridges product strategy and technical execution—encompassing responsibilities from prototyping with Claude and Workato to prioritizing roadmaps and aligning stakeholders.
This is a rare hybrid: part product leader, part AI practitioner, part governance architect. The Senior AI Product Manager owns the AI strategy in collaboration with the Lead Enterprise Architect, defining which problems AI solves vs. which need simpler solutions, and building the guardrails and policies that make AI safe, trustworthy, and compliant across Schellman. The role starts as an individual contributor—hands-on building POCs, establishing governance frameworks, and proving business impact—and will scale to leading a small AI product and engineering team as the organization grows.
This role reports to the AI COE (Center of Excellence).
ESSENTIAL FUNCTIONS
1. AI Product Strategy & Prioritization
Own Schellman's AI product roadmap for both internal operations and service delivery team automation, translating business needs into AI-powered workflows.
Partner with the Lead Enterprise Architect to carve out the AI strategy within the broader technology vision. Define the role of Claude, Workato agentic actions, Glean, and other tools in enabling intelligent automation.
Conduct rapid discovery across Finance, HR, IT, Operations, and Service Delivery teams to identify high-impact AI automation opportunities.
Determine clear build vs. buy decisions and present to the AI COE for approval: when to invest in custom AI solutions vs. leveraging vendor-embedded AI (Claude, Workday AI, Fieldguide AI, etc.) vs. platform-based automation (Workato).
Define success metrics for each AI initiative (time saved, error reduction, user adoption, business impact) and track delivery against them.
2. Hands-On AI Development & Prototyping
Write prompts, build proofs-of-concept, and prototype agentic workflows using Claude API, Workato, AWS Bedrock, and Glean.
Validate product concepts through rapid iteration—test, learn, refine with real users before scaling.
Work closely with engineers to translate POCs into production-grade solutions; able to review code, understand integration patterns, and direct technical execution.
Stay fluent in AI/LLM capabilities, limitations, and emerging techniques; continuously test new Claude features and evaluate competitive models (OpenAI, others) for fit.
Champion a culture of experimentation: encourage teams to prototype, share learnings from failures, and iterate rapidly.
3. AI Governance, Policy & Compliance
Establish AI governance frameworks that ensure responsible use of AI across the organization—covering data handling, prompt engineering standards, vendor evaluation, and risk management.
Work closely with the Compliance team to define and enforce AI usage policies for Claude.ai Enterprise, Glean, custom AI apps, and vendor-embedded AI. Ensure every AI deployment is governed before reaching customers or employees.
Lead vendor evaluation in close collaboration with procurement for AI/LLM platforms (Claude, OpenAI, others, AWS Bedrock alternatives), establishing baseline requirements for compliance, data residency, and security.
Partner with Security, Legal, and Compliance to manage AI-related risks: bias, hallucination, data leakage, regulatory requirements (especially for assessment/compliance domain).
Document and communicate AI policies, best practices, and governance decisions in close collaboration with compliance and legal to technical and non-technical stakeholders.
4. Cross-Functional Leadership & Stakeholder Alignment
Collaborate with Service Delivery, and internal Operations leaders to understand their automation needs, validate feasibility, and drive adoption of AI solutions.
Work closely with the Lead Enterprise Architect to align AI initiatives with overall platform integration strategy (Workato, APIs, data flows).
Partner with Security & Compliance teams to review AI integrations for risk and governance adherence.
Communicate AI capabilities and limitations clearly to non-technical stakeholders; help them understand when AI is the right solution.
Build consensus across teams on shared standards, tooling, and practices for AI development and deployment.
5. Team Building & Capability Development
Identify hiring needs for AI engineering, prompt engineering, and data engineering roles as the team grows beyond the initial solo PM role.
Establish best practices and rituals: AI product reviews, governance checkpoints, POC templates, and decision documentation.
Mentor engineers and product colleagues on AI/LLM fundamentals, prompt engineering, and responsible AI practices.
Build an AI product culture that emphasizes experimentation, learning from failures, and continuous improvement.
6. AI-Powered Operations & Service Delivery Impact
Deliver 5 proof-of-concept AI solutions in Year 1, with 2-3 moving to production (focused on both internal efficiency and service delivery team productivity).
Identify AI opportunities in the assessment lifecycle: evidence collection, control mapping, gap analysis, report generation, and assessor intelligence tools.
Design AI features that improve assessor efficiency and enable service delivery teams to deliver higher-quality, faster assessments (competitive advantage for Schellman).
Enable internal service delivery teams (Finance, Legal, HR, IT, Operations) to work faster and smarter with AI-assisted tools, freeing capacity for strategic work.
KNOWLEDGE, SKILLS, AND ABILITIES
Product & Strategic Thinking
Ability to define the problem before jumping to the solution. Comfort with ambiguity and rapid learning.
Strong judgment in balancing long-term vision (building scalable, governed AI platforms) with near-term wins (getting POCs to production).
Experience shipping in fast-moving environments; bias toward rapid iteration and learning from real-world feedback.
Proven ability to prioritize ruthlessly and communicate trade-offs clearly.
Technical Credibility & Hands-On Capability
Can write and iterate on prompts; understand why a prompt works or fails.
Can prototype with APIs and integration tools; comfortable reading documentation and building POCs without a full engineering team.
Able to discuss trade-offs intelligently with engineers: latency vs. cost, accuracy vs. speed, custom models vs. APIs.
Stays current on AI research, new models, and emerging capabilities; can evaluate new techniques for fit to Schellman's needs.
Not a deep ML researcher necessarily, but deeply conversant in AI concepts and limitations.
Governance & Responsibility Mindset
Proactive about building guardrails before problems occur. Thinks about downstream risks and unintended consequences.
Comfortable navigating complexity: balancing innovation with caution, speed with safety.
Ability to communicate with diverse audiences: engineers, business leaders, compliance/security teams, and end users.
Driven by trust: every AI initiative amplifies the trust we sell to our customers; if it doesn't strengthen trust, we don't do it.
Collaboration & Communication
Comfortable working cross-functionally; able to translate between technical teams and business stakeholders.
Consensus-builder: listens deeply, brings people along on tough decisions, but moves decisively once aligned.
Strong communicator: can explain complex AI concepts clearly in writing and in person.
Willing to roll up sleeves and do the work; not waiting for perfect conditions or delegating everything.
EDUCATION, WORK EXPERIENCE AND CERTIFICATIONS
Required Experience & Expertise
8–10+ years building AI/ML products or features in production environments (AI startups, FAANG, research labs, deep tech companies).
Deep hands-on expertise with Large Language Models: prompt engineering, fine-tuning, evaluation, and understanding of LLM capabilities/limitations. Shipped products powered by Claude, OpenAI, or comparable models.
Product thinking: experience translating user needs into product requirements, defining success metrics, and balancing innovation with pragmatism. Previous PM, technical founder, or AI researcher who thinks like a product leader.
Technical fluency with AI platforms and integrations: comfortable with APIs (Claude, AWS Bedrock, others), workflow orchestration (Workato), and integration patterns. Can prototype and iterate quickly.
Full-stack AI understanding: awareness of data pipelines, model evaluation, safety/governance, and the operational aspects of deploying AI. Not a data scientist necessarily, but conversant in the domain.
Strategic decision-making: ability to evaluate build vs. buy vs. integrate decisions; judgment in knowing when to use AI vs. simpler solutions.
Governance & responsible AI: familiarity with responsible AI principles, risk management, bias detection, and compliance considerations. Experience thinking through the 'downstream effects' of AI systems.
Required Certifications
None required. We value depth of demonstrated AI/ML product experience over formal credentials. However, relevant certifications in AI, cloud platforms (AWS), or product management are a plus.
Optional work experience and skills
Experience with Claude API, Anthropic's models, or deep familiarity with Claude's capabilities.
Hands-on experience with Workato, workflow automation, or low-code integration platforms.
Knowledge of AWS Bedrock or other managed LLM services.
Prompt engineering expertise and familiarity with prompt optimization techniques.
Experience in professional services, compliance, or assessment domains (strong bonus—helps rapid domain learning).
Data governance, knowledge management, or data product experience.
Security, compliance, or audit background—understanding of regulated industries and governance frameworks.
Experience in professional services, consulting, audit, or compliance-driven organizations.
Familiarity with Schellman's platforms: HubSpot, Workday, Kantata, Fieldguide, Ironclad, Workato.
Experience building agentic AI systems or multi-step workflows.
Knowledge of evaluation frameworks for LLMs and methods for testing prompt/model quality.
Background in data governance, knowledge management, or information architecture.
Experience establishing AI governance or responsible AI practices in organizations.
Track record of building and scaling product or engineering teams.
Deep familiarity with AWS services, Glean, or other enterprise AI platforms.
Schellman is an equal opportunity employer (EOE) and strongly supports diversity in the workplace; therefore, providing equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, national origin, protected veteran status, disability status, or any other legally protected basis, including arrest and conviction records, in accordance with applicable law. Schellman uses E-Verify in our hiring process.
At Schellman, we strive to provide a flexible and balanced environment and therefore offer the opportunity to work remotely, unless otherwise stated in the job requirements. Connecting, collaborating and continuous education are also highly valued and therefore we require some travel annually for our Internal Service Delivery roles, which can include in-person training, team meet-ups, and strategy meetings. Service Delivery team members will also be required to travel based on business and client needs.
