AI Product Stream Lead
Cargoo · Yerevan
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.