Builder Product Manager, AI Platform & Agentic Workflows

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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.

- 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.