Engagement Manager, AI Implementations

As part of the Technology Distribution team of Tether Data, we are seeking an Engagement Manager to lead the implementation of AI‑driven initiatives powered by QVAC, Tether’s SDK for local AI and delegated inference. The role focuses on orchestrating complex, multi‑stakeholder deployments of decentralized AI solutions across enterprise, government, and institutional environments.

This position combines client engagement, technical coordination, and delivery oversight to ensure successful onboarding and execution of AI use cases, ranging from edge AI deployments to privacy‑preserving inference systems. The Engagement Manager plays a central role in aligning client objectives with product capabilities, coordinating internal and external stakeholders, and driving implementations from initial scoping through production rollout.

Responsibilities

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 the 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 for 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.

Core Experience

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.

Technical Understanding

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.

Communication & Execution

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.

Nice to Have

Experience with decentralized technologies, blockchain, or privacy‑preserving systems.

Exposure to AI infrastructure tooling or MLOps workflows.

Experience in regulated industries or public sector projects.

Basic familiarity with programming or scripting environments (Python, APIs, CLI tools).

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