Engagement Manager, AI Implementations

🕒 July 21

🇵🇱 Poland – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Artificial Intelligence

👻 Ghost score 39%

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Logo of Tether.to

Tether.to

11 - 50 employees

Founded 2014

₿ Crypto

💳 Fintech

💸 Finance

Crypto • Fintech • Finance

Tether. to is a leading digital asset company that pioneers the use of stablecoins in the blockchain space. As the most widely adopted stablecoin, Tether tokens are designed to be pegged 1-to-1 with fiat currencies, offering a stable digital asset option for users. The platform facilitates these token transactions across multiple blockchains, enhancing cross-border transactions while maintaining transparency with daily records of total assets and reserves. Tether's initiatives include educational programs promoting digital asset usage, especially targeting regions like the Middle East, Turkey, and the Philippines. Tether thus positions itself as a disruptor in the traditional financial system by enabling a stable, efficient method of handling transactions in the digital currency world.

📋 Description

• Act as the primary point of contact for clients and partners during AI implementation initiatives, ensuring clear communication, expectation alignment, and structured execution. • Guide clients in translating business requirements into AI deployment strategies leveraging QVAC capabilities (local inference, delegated compute, privacy-preserving architectures). • Lead end-to-end coordination of QVAC-based implementations from kickoff through production deployment. • Define implementation roadmaps, milestones, and dependencies across AI models, infrastructure, and integration layers. • Coordinate closely with product, engineering, and research teams to align on QVAC capabilities, limitations, and roadmap evolution.

🎯 Requirements

• 5+ years of experience in program management, technical account management, or delivery roles within AI, data infrastructure, or complex technology environments. • Proven experience managing cross-functional implementations involving multiple stakeholders (internal teams, clients, external partners). • Familiarity with AI/ML deployment concepts, including model inference, edge/local AI, and distributed compute architectures. • Strong communication and stakeholder management skills, including experience working with enterprise or government entities. • Exposure to AI infrastructure tooling or MLOps workflows is a nice to have.

🏖️ Benefits

• Flexible working arrangements • Professional development opportunities

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