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Cargoo

AI Product Stream Lead

Yerevan, Armenia

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hirly's read of this role

Role family
Product management
Seniority
Lead / management
Country
AM
Work mode
On-site / unstated
First seen by hirly
30 Sept 2026

Derived automatically from the posting. Upload your resume above to see how the role scores against it.

the posting

Role Purpose

The AI Product Stream Lead defines and delivers AI-powered supply chain solutions that solve real customer and operations problems on the Cargoo platform. You own a product stream end to end, from framing where AI creates value (automation, decision support, agents) to shipping it and proving it works in production.

You lead a cross-functional team, balancing customer value, technical and data feasibility, AI quality and cost, business priorities and delivery timelines. You also help set how Cargoo builds product in an AI-native way.

Key Responsibilities

AI Product Vision & Strategy

Shape the stream’s product vision and strategy with stakeholders, in line with Cargoo’s AI strategy and customer needs.

Identify and prioritize AI opportunities across freight operations (e.g. exception handling, quoting, document processing, planning and execution support) by value, feasibility and risk.

Decide where AI should automate, where it should assist, and where a human must stay in control.

Own product decisions, balancing customer value, technical feasibility, AI quality, cost, business priorities and delivery timelines.

Leadership & Delivery

Lead the development team through the whole product lifecycle, from discovery to rollout and adoption.

Decide scope, schedule and quality, including when an AI feature is good enough to ship and when it needs another iteration.

Run fast discovery loops: prototype with AI tools, validate with real users and data, then commit.

Make sure the team meets internal policies, data protection rules and responsible-AI standards (incl. EU AI Act awareness).

Product Management & Requirements

Create clear Use Cases, Requirements and Business Rules. For AI features, add expected behaviour, edge cases, failure modes and escalation paths.

Define success and evaluation criteria for AI features with engineering: accuracy, latency, cost per task and acceptable error rates, backed by representative test sets.

Write User Stories and Acceptance Criteria that cover both deterministic logic and probabilistic AI behaviour.

Work with engineering and data owners on data readiness: which data we need, its quality, and who owns it.

Own and continuously refine the Product Backlog so the team always works on the highest-value opportunities.

Track adoption and impact after release (time saved, automation rate, user trust) and feed the learning back into the roadmap.

AI-Native Ways of Working

Use AI tools every day for research, specs, prototyping, data analysis and feedback synthesis.

Help the team and wider product org adopt AI-assisted delivery practices.

Candidate Profile

Education & Experience

5+ years managing software development projects, strongly preferably as a Product Owner.

Proven managerial skills: team leadership, decision-making and conflict resolution.

Degree or diploma in Information Technology, Informatics or a related field.

Technical Skills (Must Have)

Deep understanding of Agile values, principles and frameworks (Scrum, Kanban, Lean) and the ability to apply them well.

Strong analytical skills, comfortable working with data to make product decisions.

Solid working understanding of how LLMs and AI agents work, what they are good at, and where they fail (hallucinations, non-determinism, cost, latency).

Hands-on, daily use of AI tools in your own work.

Fluent English, written and spoken.

Strong Plus

Having shipped at least one AI/ML or LLM-based feature to production.

Experience defining evaluation criteria or test sets for AI features.

Logistics background or experience with logistics solutions.

Experience with data products, workflow automation or decision-support systems.

Behavioral Traits

Excellent communication with technical and non-technical audiences, including customers, executives, managers and subject matter experts. Able to explain AI trade-offs in plain language.

Strong leadership and decision-making, able to guide teams and resolve conflicts.

Analytical, structured and value-driven in prioritization. Evidence over hype.

Curious and fast-learning, comfortable with ambiguity and quick iteration.

Original posting on Cargoo's site ↗

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