Job Description:Articul8 AI is hiring a
Builder Product Manager, AI Platform & Agentic Workflows to own the delivery of customer-facing AI workflow experiences: multi-agent missions, natural-language query over domain knowledge graphs, SDK-based hyper-personalized applications, agents, and MCP-enabled tools.
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.
