JobHire.AI

Customer Support

• Proactively engage users via **chat** and **calls** during onboarding and early usage;
• Reach out to users showing low activity or early churn signals and help them get back on track;
• Act as a **human recovery layer**: understand user frustration, diagnose issues, and rebuild trust through real conversation;
• Help users understand what can be improved to get better results, using clear and empathetic communication;
• Recommend profile, CV, or job search improvements, including relevant paid add-ons (e.g. resume review) when they add real value;
• Share user feedback and recurring patterns with Product to improve the overall customer experience.

Customer Support Agent, PST-EST timezone

Customer Support Agent – PST-EST

Product Manager

• 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), choose the right validation method (A/B test, shadow model, phased rollout), and make data-driven go/no-go decisions under uncertainty.
• Apply critical thinking to decompose complex, ambiguous problems.
• Collaborate closely with Engineering, Data Science, and business teams.

Lead Tech AI/ML Product Manager

• 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.
• Collaborate closely with Engineering, Data Science, and business teams.
• Design and execute rapid, pragmatic validation cycles.

Senior Tech Product Manager – AI/ML

• 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.

Senior Tech AI/ML Product Manager – Job Hunt Engine

• 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.
• Apply critical thinking to decompose complex, ambiguous problems.
• Collaborate closely with Engineering, Data Science, and business teams.

• 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), choose the right validation method (A/B test, shadow model, phased rollout), and make data-driven go/no-go decisions under uncertainty.
• 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.