Project Manager (Discovery/MVP with AI)
Glorium Technologies · Europe ·
- Work mode
- Remote
- Category
- Project management
- Experience
- 3+ years
Glorium Technologies · Europe ·
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Leads product discovery and AI-driven MVP projects for clients: running workshops, scoping MVPs, and guiding cross-functional teams (AI engineers, designers, devs) using GenAI tools like OpenAI API, Claude, LangChain, and Agile/Lean methods. Needs 3+ years IT PM experience in an agency/outsourcing setting, a technical background, and upper-intermediate English; some US travel required.
We are looking for a strategic, highly technical Project Manager specializing in Product Discovery and AI-driven MVPs. In this role, you will bridge the gap between complex client business goals and cutting-edge GenAI/AI solutions. You will lead cross-functional engineering teams through the initial phases of the product lifecycle — transforming ambiguous ideas into validated AI prototypes and production-ready MVPs.
Does this relate to you?
3+ years of Project Management experience in an IT agency, software studio, or outsourcing environment with a focus on discovery-to-MVP launches.
Hands-on background in a technical role (Software, QA, Data Engineer, or Technical Analyst) with a strong understanding of modern LLM architectures, APIs, and data pipelines.
Practical experience using AI tools and ecosystems (OpenAI API, Claude, LangChain, vLLM, or Hugging Face) for prototyping or workflow automation.
Mastery of Lean Startup, Design Sprints, and Agile frameworks (Scrum, Kanban) tailored for rapid scoping and high-uncertainty environments.
Exceptional communication and negotiation skills for leading client workshops and managing non-technical stakeholders.
Upper-Intermediate or Advanced level of English (spoken and written).
Willingness and ability to travel to the US for business trips/client meetings as needed.
A new team member will be in charge of:
Leading product discovery, client consultations, and pre-sales technical proposal preparation.
Defining MVP scope, prioritizing backlogs, and managing rapid iteration cycles with cross-functional teams (AI engineers, UI/UX designers, developers).
Evaluating technical feasibility, data readiness, and model constraints in close collaboration with AI architects.
Managing client expectations by translating complex AI concepts into clear business ROI and realistic delivery timelines.
Identifying risks (data quality, scope creep, API dependencies) and applying mitigation strategies to ensure cost-effective delivery.
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