Engagement Manager, AI Implementations

🕒 May 8

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Tether.to

WebsiteLinkedInAll Job Openings

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). • Support Expansion team in shaping AI-related opportunities by providing input on feasibility, integration complexity, and delivery approach. • Identify opportunities to extend implementations across additional use cases, geographies, or Tether technologies. • 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. • Ensure alignment between client expectations and actual product capabilities, avoiding scope drift or mispositioning. • Track progress across multiple concurrent AI deployments, ensuring timely delivery and readiness for production environments. • Coordinate closely with product, engineering, and research teams to align on QVAC capabilities, limitations, and roadmap evolution. • Facilitate integration between client systems and QVAC components, including model deployment pipelines, APIs, and compute environments. • Work with legal and compliance teams where required, particularly in sensitive AI deployments involving data locality or privacy constraints. • Maintain structured communication flows across all stakeholders involved in the implementation lifecycle. • Establish and maintain governance frameworks including implementation plans, risk tracking, and decision logs. • Produce executive-level updates summarizing progress, risks, blockers, and next steps. • Ensure documentation of implementation architectures, deployment patterns, and key learnings for reuse across future projects. • Support escalation management and ensure timely resolution of technical or operational challenges.

🎯 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). • Strong ability to operate at the intersection of technical and business domains. • Familiarity with AI/ML deployment concepts, including model inference, edge/local AI, and distributed compute architectures. • Understanding of APIs, SDK integrations, and system architecture patterns. • Ability to engage in technical discussions with engineering teams while maintaining business-level clarity with clients. • Strong communication and stakeholder management skills, including experience working with enterprise or government entities. • Structured approach to project tracking, documentation, and governance. • Ability to manage ambiguity and operate in fast-evolving, early-stage environments. • Experience with decentralized technologies, blockchain, or privacy-preserving systems (nice to have). • Exposure to AI infrastructure tooling or MLOps workflows (nice to have). • Experience in regulated industries or public sector projects (nice to have). • Basic familiarity with programming or scripting environments (Python, APIs, CLI tools) (nice to have).

🏖️ Benefits

• Flexible work arrangements • Professional development opportunities