Articul8 - Builder Product Manager, AI Platform & Agentic Workflows
Requirements
• 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. • Experience creating product videos or demo walkthroughs. • 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. • Practice Humility: • 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. • Bias for Outcomes: • 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. • Care Deeply: • 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. • Dare to Do the Impossible & Embrace Scarcity: • 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. • Build a Better World:
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.
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