AI Technical Product Manager at YOOBIC

, , Morocco -

Full Time

Start Date

Immediate

Expiry Date

20 May, 26

Salary

0.0

Posted On

19 Feb, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, ML Foundations, LLM Prototyping, Prompting, API, Feature Engineering, Data-Centric ML, Model Selection, Error Analysis, User Interface Development, Experimentation, Product Mindset, FastAPI, Docker, Cloud, SaaS Product Management

Industry

Software Development

Description

Who we are… YOOBIC is an AI-powered frontline employee experience platform. Our mobile app gives business leaders and frontline teams the performance tools they need to communicate, learn, and work—all in one place. With streamlined communications, mobile learning, and digitized task management, YOOBIC drives operational excellence while drastically improving the frontline employee working experience. 350+ companies around the world including MattressFirm, Michaels, GameStop, Lacoste, Lidl, Boots, Peloton, Pret, Puma, and Vans trust YOOBIC to improve operational consistency and agility, get real-time visibility into multi-location business execution, and improve customer experience. To learn more about YOOBIC, visit www.yoobic.com or follow us on LinkedIn. What we need… We’re hiring a AI Tech Product Manager to rapidly prototype and validate AI-powered product ideas, then handover to ML Engineering for industrialization. You’ll pair a product mindset with hands-on Python, data‑centric ML, and LLM‑centric prototyping (prompting via APIs, chaining treatments, and building simple user interfaces for user testing) to turn promising concepts into testable, value‑proven prototypes—fast. You’ll collaborate closely with Product Managers and Designers, and work with ML Engineers, our AI/ML Architect, and AI/ML Researcher to bring the right ideas to life. What you'll do... Scope opportunities with PMs and Designers; frame problems and success criteria before solutioning. Prototype quickly using two equally important approaches: LLM‑centric prototyping: prompt LLMs via API, chain treatments/tools/agents, and develop simple user interfaces to enable rapid user testing and feedback loops. Data‑centric ML in Python: feature engineering, dataset curation, model selection, training, evaluation, and error analysis to validate feasibility and impact beyond “just calling an API.” Assess feasibility & value (technical + product), run lightweight experiments, and capture learnings. Translate wins into briefs & delivery plans and handover to ML Engineers for productionization; partner through design/QA. Track impact post‑launch with the product team (adoption, quality, and business KPIs). What you have (must haves)... Strong Python and ML foundations; able to build workable prototypes (NumPy/pandas/scikit‑learn; PyTorch a plus). Proven feature engineering and data‑centric ML skills (problem decomposition, robust evaluation, error analysis). Equally strong LLM prototyping skills: prompting via APIs, tool/agent orchestration and chaining, and building simple UIs (e.g., lightweight web apps or internal panels) for user testing. Experience with no‑code/workflow tools (e.g., n8n) to accelerate experiments; solid prompt/agent design. Product mindset: ability to frame problems, run experiments, and communicate clearly with technical and non‑technical partners. Excellent English communication; proactive, self‑organized remote collaborator Nice to have... Familiarity with FastAPI/Docker/cloud for local validation (you won’t own MLOps/industrialization). Prior experience in SaaS product management or technical product roles.

Responsibilities

The role involves rapidly prototyping and validating AI-powered product ideas using both LLM-centric methods (API prompting, chaining) and data-centric ML in Python, followed by translating successful concepts into production-ready briefs for ML Engineers. Responsibilities also include scoping opportunities with design and product teams, assessing technical and product feasibility, running lightweight experiments, and tracking post-launch impact metrics.