Tether.io

3 days ago

Engagement Manager, AI Implementations

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 6 hours agoStatus: Live

Job Summary

Lead end-to-end implementation of AI-driven initiatives across enterprise, government, and institutional environments. Serve as the primary client/partner contact while translating requirements into AI deployment strategies using QVAC (local inference and delegated inference). Coordinate cross-functional internal teams to design roadmaps, manage dependencies, and ensure delivery readiness—from scoping through production rollout. Maintain governance, executive reporting, and implementation documentation to support reuse of architectures and learnings.

Location: Berlin

Workplace: Remote

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Act as primary point of contact for clients and partners during AI implementation initiatives, ensuring clear communication and expectation alignment.
•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, including roadmaps, milestones, and dependencies.
•Coordinate closely with product, engineering, and research teams to align on QVAC capabilities, limitations, and roadmap evolution; facilitate client system integration via model deployment pipelines and APIs.
•Establish governance frameworks, track multiple concurrent deployments, produce executive-level updates, and document architectures, deployment patterns, and learnings.

Key Requirements

•5+ years in program management, technical account management, or delivery roles within AI, data infrastructure, or complex technology environments.
•Experience managing cross-functional implementations with internal teams, clients, and external partners.
•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, with ability to support technical discussions with engineering teams.
•Strong communication, stakeholder management, and a structured approach to project tracking, documentation, and governance (enterprise or government experience).

Experience:5+ yearsAIData infrastructureBlockchainDecentralized systemsPrivacy-preserving systemsMLOps

Education:Bachelor's

Skills:Stakeholder managementStructured executionGovernanceCommunicationManaging ambiguity

Languages:English

Tech Stack:QVACSDKAI/MLModel inferenceEdge AILocal inferenceDelegated inferencePrivacy-preserving architecturesAPIsMLOpsPythonCLI toolsBlockchainDecentralized technologies

Company Brief

Tether.io

Builds tools and services to help companies hire, onboard, and manage remote or distributed teams across borders, focusing on payroll, compliance, and global employment workflows.

Industry: HR Tech

Website

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Tether.io

3 days ago

Engagement Manager, AI Implementations

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 6 hours agoStatus: Live

Job Summary

Location: Berlin

Workplace: Remote

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Act as primary point of contact for clients and partners during AI implementation initiatives, ensuring clear communication and expectation alignment.
•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, including roadmaps, milestones, and dependencies.
•Coordinate closely with product, engineering, and research teams to align on QVAC capabilities, limitations, and roadmap evolution; facilitate client system integration via model deployment pipelines and APIs.
•Establish governance frameworks, track multiple concurrent deployments, produce executive-level updates, and document architectures, deployment patterns, and learnings.

Key Requirements

•5+ years in program management, technical account management, or delivery roles within AI, data infrastructure, or complex technology environments.
•Experience managing cross-functional implementations with internal teams, clients, and external partners.
•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, with ability to support technical discussions with engineering teams.
•Strong communication, stakeholder management, and a structured approach to project tracking, documentation, and governance (enterprise or government experience).

Experience:5+ yearsAIData infrastructureBlockchainDecentralized systemsPrivacy-preserving systemsMLOps

Education:Bachelor's

Skills:Stakeholder managementStructured executionGovernanceCommunicationManaging ambiguity

Languages:English

Tech Stack:QVACSDKAI/MLModel inferenceEdge AILocal inferenceDelegated inferencePrivacy-preserving architecturesAPIsMLOpsPythonCLI toolsBlockchainDecentralized technologies

Company Brief

Tether.io

Builds tools and services to help companies hire, onboard, and manage remote or distributed teams across borders, focusing on payroll, compliance, and global employment workflows.

Industry: HR Tech

Website