Associate Product Manager, Internal AI

AssetMark•Charlotte, NC

1d•$77,000 - $97,000•Hybrid

About The Position

AssetMark’s AI Tiger Team is transforming how its 1,000+ employees work. As Associate Product Manager, Internal AI, you will join this cross-functional team and help identify, prototype, launch, and improve AI tools that increase productivity, streamline workflows, and enhance the employee experience. This is an early-career product role for someone with strong technical curiosity and extensive hands-on experience using AI to automate, accelerate, or improve real-world work. You should be an active builder who experiments with AI tools, creates prototypes, connects systems, tests ideas quickly, and learns by doing. Working closely with the Director of Product, Internal AI, engineers, data professionals, and business teams, you will turn employee workflow problems into practical AI solutions. You will contribute directly to building prototypes, agents, automations, and internal tools—not only coordinate their development.

Requirements

Strong technical fluency and curiosity about generative AI, LLMs, agents, APIs, integrations, automation, and data flows.
Demonstrated hands-on use of AI to automate, expedite, or improve real-world workflows.
Ability and enthusiasm to build working prototypes rather than only write requirements or coordinate others.
Foundational product management skills, including discovery, prioritization, testing, launch planning, and measurement.
Ability to turn ambiguous workflow problems into practical experiments and product ideas.
Strong analytical, problem-solving, and organizational skills.
Clear written and verbal communication with technical and non-technical audiences.
Comfort learning new tools quickly and working in an environment where solutions are not yet well defined.
Sound judgment regarding responsible AI, data security, privacy, compliance, and enterprise risk.
Empathy for users and a strong interest in improving how employees work.
Bachelor’s degree in Business, Computer Science, Engineering, Information Systems, Data Science, Economics, Finance, or a related field, or equivalent practical experience.
0–3 years of experience in product management, technology, software development, data, operations, consulting, automation, or a related field.
Experience building or contributing to AI tools, automations, internal products, prototypes, or technology-enabled workflow improvements through work, internships, academic projects, or independent projects.
Hands-on experience with generative AI tools, AI agents, APIs, automation platforms, analytics tools, or low-code/no-code development.

Nice To Haves

Experience in financial services, fintech, wealth management, or another regulated industry is a plus.

Responsibilities

Interview employees, observe workflows, and identify high-value opportunities for AI and automation.
Map current processes and uncover repetitive, slow, manual, or error-prone work.
Evaluate opportunities based on user value, business impact, feasibility, risk, and effort.
Help define product goals, MVP scope, requirements, success metrics, and launch plans.
Research new AI tools, models, platforms, and automation approaches that may benefit AssetMark.
Rapidly build prototypes, proofs of concept, AI agents, automations, prompts, workflows, and lightweight internal tools.
Use AI personally and extensively to improve research, analysis, documentation, communication, testing, and operational workflows.
Experiment with LLMs, APIs, automation platforms, integrations, data sources, and low-code or no-code tools.
Translate promising prototypes into clear product concepts that engineering teams can harden, scale, and support.
Partner closely with engineers while remaining actively involved in design, configuration, testing, troubleshooting, and iteration.
Evaluate AI output quality, reliability, usability, and operational fit.
Help investigate issues involving data, integrations, permissions, accuracy, security, or performance.
Gather employee feedback before and after launches and use it to improve products quickly.
Support pilots, demonstrations, onboarding, office hours, training, and launch communications.
Create clear documentation, playbooks, FAQs, and self-service resources.
Help employees understand when and how to use AI tools safely and effectively.
Track adoption, repeat usage, satisfaction, time saved, quality improvement, and cycle-time reduction.
Turn usage data and employee feedback into practical product improvements.
Work with Product Engineering, Data, IT, Security, Compliance, Legal, Risk, and business teams to deliver secure, useful AI capabilities.
Coordinate dependencies, decisions, testing, risks, and delivery activities.
Prepare product demonstrations, progress updates, dashboards, and recommendations for stakeholders.
Help establish repeatable methods for evaluating, launching, and scaling internal AI solutions.
Reinforce responsible AI, data privacy, security, and compliance requirements.