Builder Product Manager, AI Platform & Agentic Workflows

This is a hands-on, “build-and-ship” product role. You will work directly with customers and internal teams to prototype solutions, validate workflows end-to-end, troubleshoot issues, and translate real-world needs into clear, actionable requirements for engineering and applied research. You will use a state-of-the-art AI platform and feed customer requirements directly to the platform teams to improve both the end-use application and base platform capabilities.

Key Responsibilities

Customer Ownership & Product Delivery

Serve as the customer-facing owner for delivering agentic missions for assigned enterprises, including weekly demos, product updates, technical Q&A, and domain/SME working sessions.

Translate customer needs into product requirements, technical feature requests, acceptance criteria, edge cases, data dependencies, and test scenarios.

Drive delivery from prototype to production-ready quality, balancing customer outcomes with platform constraints and timelines.

Distill customer requests, bugs, platform gaps, and technical constraints into prioritized delivery plans.

Hands-On Building & Prototyping

Build mission-specific experiences using the Articul8 platform, SDK, agents, APIs, and MCP-enabled tools.

Create prototypes, demos, internal tools, interaction flows, and proof-of-concept applications to make customer workflows tangible and testable.

Identify what can be delivered with existing capabilities, where SDK extensions are required, and where platform gaps require new engineering investment.

Testing, Quality & Troubleshooting

Own the quality bar across dev, staging, and customer-facing environments.

Test end-to-end workflows (agent outputs, model behavior, tool calls, data ingestion, UI flows, knowledge grounding, and SDK experiences).

Reproduce failures, isolate root causes, and file high-signal bug reports and triage notes for engineering.

Partner closely with the engineering team to validate fixes and confirm what has landed in source vs. what has been deployed and verified.

Differentiate among product gaps, data issues, model behavior, agent orchestration failures, tool-calling problems, UI gaps, environment mismatches, and engineering defects.

Applied Research Collaboration

Partner with applied research to clarify data needs for training, evaluation, fine-tuning, and experimentation.

Help collect, organize, and document datasets needed for training and testing.

Support the data creation process by defining realistic workflow scenarios, edge cases, expected outputs, and failure modes.

Test models, inspect outputs, compare results against expectations, and provide structured feedback.

Documentation, Competitive Analysis & Communication

Produce high-quality product and technical documentation: requirements, workflow guides, SDK notes, test plans, troubleshooting guides, handover docs, release notes, and demo scripts.

Analyze competitor offerings across enterprise AI platforms, agents, MCP ecosystems, workflow automation, knowledge graphs, and evaluation tooling; translate insights into product recommendations.

Create customer-ready materials including walkthroughs, demos, technical explainers, and presentations.

Mentor interns with structured feedback and product judgment.

Required Qualifications

4+ years of overall professional experience in product management (or product-adjacent technical roles such as engineering, solutions engineering, or technical implementation), with a demonstrated track record of shipping complex platform products end-to-end.

Strong technical foundation across software systems, APIs, SDKs, data workflows, AI applications, enterprise platforms, and/or cloud-based systems.

Hands-on experience building prototypes, workflow applications, demos, internal tools, agentic workflows, or customer proof-of-concepts.

Familiarity with agents and tool-calling (including MCP-style tools), LLM applications, RAG, knowledge systems, and AI copilots.

Ability to translate ambiguous customer needs into clear technical requirements and acceptance criteria for engineering.

Demonstrated ability to test product features end-to-end, troubleshoot issues, reproduce bugs, and provide structured feedback.

Strong written and verbal communication, documentation skills, and comfort presenting to technical and non-technical stakeholders.

Comfort operating in a fast-moving environment with evolving customer needs, research priorities, and platform capabilities.

Preferred Qualifications

Experience running customer demos, stakeholder updates, and technical product walkthroughs.

Experience reading source code, reviewing repositories, and validating whether fixes have landed across branches/environments.

Experience creating synthetic datasets, evaluation sets, prompt test suites, or domain-specific training examples.

Experience partnering with researchers, data scientists, ML engineers, or applied AI teams.

Experience with knowledge graphs, natural-language query systems, agentic copilots, RAG workflows, or AI evaluation.

Exposure to enterprise domains such as manufacturing, energy, financial services, semiconductors, telecom, healthcare, industrial operations, supply chain, or engineering workflows.

Experience creating product videos or demo walkthroughs.

MBA preferred but not required.

Professional Attributes (Code42):

Practice Humility: You ask questions even when you think you know the answer. You seek feedback early, learn from anyone regardless of title, and treat every experiment — especially the failures — as data.

Bias for Outcomes: You measure your work by what changed, not what you tried. You ship results, not slide decks. When a deadline is real, you find a way.

Care Deeply: You treat every problem as yours to solve. You review your own work with the rigor you'd want from a reviewer. You help teammates without being asked.

Dare to Do the Impossible & Embrace Scarcity: You set goals that make you uncomfortable. When told something can't be done, you find a way or a better question. Constraints sharpen your thinking, not slow it down.

Build a Better World: You believe AI should make things meaningfully better for real people. You hold yourself accountable not just for whether your model works, but for what it does in the world.