Senior Tech AI/ML Product Manager – Job Hunt Engine

Responsibilities

• Define the vision, strategy, and roadmap for AI/ML product features (JobHunt Engine).
• Take full ownership of the product lifecycle from hypothesis to scaled impact, focusing on business results, not just model performance.
• Translate business problems into ML hypotheses and solutions.
• Work side-by-side with ML engineers and data scientists to define data requirements, evaluation frameworks (evals, RAG, agents), model monitoring, and delivery processes.
• Design and execute rapid, pragmatic validation cycles.
• Formulate clear hypotheses (Problem → Mechanism → Impact → Metric) and choose the right validation method (A/B test, shadow model, phased rollout).
• Apply critical thinking to decompose complex, ambiguous problems.
• Collaborate closely with Engineering, Data Science, and business teams.
• Communicate complex ML concepts clearly and align stakeholders on goals, trade-offs, and progress.

Requirements

• Strong understanding of ML/LLM fundamentals (NLP, recommendation systems, etc.).
• Hands-on experience building and scaling AI-powered features (matching, ranking, personalization).
• Practical knowledge of modern AI/ML concepts: evaluation frameworks, RAG, agents, model monitoring.
• Ability to define data pipelines, metrics, and work processes with ML engineering teams.
• Nice to have: hands-on experience in a Data Science, Data Analyst, or ML Engineer role.
• 5+ years of experience in a Product Manager role, preferably in a data-intensive or ML-driven domain (HRTech experience is a strong plus).
• Proven ability to formulate and rigorously test product/ML hypotheses using statistical methods (A/B testing, significance, confidence intervals).
• Ability to reason about probability, causality, and data limitations to make informed decisions.
• Entrepreneurial & Hands-on: "Let's build it" attitude. Able to create quick prototypes and test ideas without over-engineering. Comfortable with "building with sticks and glue" to learn fast.
• Outcome-Oriented: Owns the business result, not just the AI model. Pragmatic and willing to simplify or kill features that don't drive impact.
• Thrives in Ambiguity: Can navigate uncertainty, contradictory model results, and noisy data. Structured thinker who can bring clarity to complex situations.
• Fluent English and Russian. Excellent ability to communicate with technical (Engineers, Data Scientists) and non-technical stakeholders.