Engagement Manager, AI Implementations

Tether Operations Limited

20h•Remote

About The Position

The Engagement Manager, AI Implementations combines client engagement, technical coordination, and delivery oversight to support successful onboarding and execution of AI use cases, ranging from edge AI deployments to privacy-preserving inference systems. The role aligns client objectives with product capabilities, coordinates internal and external stakeholders, and drives implementations from initial scoping through production rollout. The position leads coordination of QVAC-based implementations, including defining roadmaps and milestones, tracking dependencies and delivery progress, and ensuring implementations are ready for production. It also facilitates integration between client systems and QVAC components, supports governance and escalation management, and documents architectures, deployment patterns, risks, decisions, and key learnings. The role works closely with clients, partners, product, engineering, research, legal, and compliance teams.

Requirements

At least 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, including internal teams, clients, and external partners.
Ability to work across technical and business domains.
Familiarity with AI/ML deployment concepts, including model inference, edge or local AI, and distributed compute architectures.
Understanding of APIs, SDK integrations, and system architecture patterns.
Ability to participate in technical discussions with engineering teams while communicating clearly at a business level 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 work in fast-evolving, early-stage environments.

Nice To Haves

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, such as Python, APIs, or CLI tools.

Responsibilities

Serve as the primary point of contact for clients and partners during AI implementation initiatives, maintaining clear communication, aligned expectations, and structured execution.
Help clients translate business requirements into AI deployment strategies using QVAC capabilities, including local inference, delegated compute, and privacy-preserving architectures.
Support the Expansion team by advising on the feasibility, integration complexity, and delivery approach for AI-related opportunities.
Identify opportunities to extend implementations to 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.
Keep client expectations aligned with product capabilities and prevent scope drift or mispositioning.
Track progress across concurrent AI deployments and support timely delivery and production readiness.
Coordinate with product, engineering, and research teams 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 when required, especially for deployments involving data locality or privacy constraints.
Maintain structured communication across stakeholders throughout the implementation lifecycle.
Establish and maintain governance frameworks, including implementation plans, risk tracking, and decision logs.
Prepare executive-level progress updates covering risks, blockers, and next steps.
Document implementation architectures, deployment patterns, and key learnings for reuse.
Support escalation management and timely resolution of technical or operational challenges.