Shape and lead the development of a next-generation AI product ecosystem for business operations, owning the architecture and technical direction of production-grade agentic systems.
Compensation
Competitive annual salary: $200,000–$300,000, depending on experience, skills, and qualifications.
Responsibilities
Lead the architecture, design, and development of a production-grade multi-agent system capable of safely executing actions across core business tools, including ERP, CRM, spreadsheets, calendars, email, analytics, and compliance platforms.
Own the development and continuous improvement of Model Context Protocol (MCP) integrations that connect internal business systems with external AI environments and assistants.
Design and implement a unified data infrastructure that consolidates information from multiple sources into a centralized layer accessible to AI agents and external tools.
Build contextual memory and customer profiling systems that ingest business data and enable increasingly personalized, intelligent AI experiences.
Establish robust reliability, observability, permission management, and durable execution mechanisms to ensure agents operate safely on real customer data.
Shape and lead the development of a next-generation AI product ecosystem for business operations, owning the architecture and technical direction of production-grade agentic systems.
Compensation
Competitive annual salary: $200,000–$300,000, depending on experience, skills, and qualifications.
Responsibilities
Lead the architecture, design, and development of a production-grade multi-agent system capable of safely executing actions across core business tools, including ERP, CRM, spreadsheets, calendars, email, analytics, and compliance platforms.
Own the development and continuous improvement of Model Context Protocol (MCP) integrations that connect internal business systems with external AI environments and assistants.
Design and implement a unified data infrastructure that consolidates information from multiple sources into a centralized layer accessible to AI agents and external tools.
Build contextual memory and customer profiling systems that ingest business data and enable increasingly personalized, intelligent AI experiences.
Establish robust reliability, observability, permission management, and durable execution mechanisms to ensure agents operate safely on real customer data.
Shape and lead the development of a next-generation AI product ecosystem for business operations, owning the architecture and technical direction of production-grade agentic systems.
Compensation
Competitive annual salary: $200,000–$300,000, depending on experience, skills, and qualifications.
Responsibilities
Lead the architecture, design, and development of a production-grade multi-agent system capable of safely executing actions across core business tools, including ERP, CRM, spreadsheets, calendars, email, analytics, and compliance platforms.
Own the development and continuous improvement of Model Context Protocol (MCP) integrations that connect internal business systems with external AI environments and assistants.
Design and implement a unified data infrastructure that consolidates information from multiple sources into a centralized layer accessible to AI agents and external tools.
Build contextual memory and customer profiling systems that ingest business data and enable increasingly personalized, intelligent AI experiences.
Establish robust reliability, observability, permission management, and durable execution mechanisms to ensure agents operate safely on real customer data.
Shape and lead the development of a next-generation AI product ecosystem for business operations, owning the architecture and technical direction of production-grade agentic systems.
Compensation
Competitive annual salary: $200,000–$300,000, depending on experience, skills, and qualifications.
Responsibilities
Lead the architecture, design, and development of a production-grade multi-agent system capable of safely executing actions across core business tools, including ERP, CRM, spreadsheets, calendars, email, analytics, and compliance platforms.
Own the development and continuous improvement of Model Context Protocol (MCP) integrations that connect internal business systems with external AI environments and assistants.
Design and implement a unified data infrastructure that consolidates information from multiple sources into a centralized layer accessible to AI agents and external tools.
Build contextual memory and customer profiling systems that ingest business data and enable increasingly personalized, intelligent AI experiences.
Establish robust reliability, observability, permission management, and durable execution mechanisms to ensure agents operate safely on real customer data.
Shape and lead the development of a next-generation AI product ecosystem for business operations, owning the architecture and technical direction of production-grade agentic systems.
Compensation
Competitive annual salary: $200,000–$300,000, depending on experience, skills, and qualifications.
Responsibilities
Lead the architecture, design, and development of a production-grade multi-agent system capable of safely executing actions across core business tools, including ERP, CRM, spreadsheets, calendars, email, analytics, and compliance platforms.
Own the development and continuous improvement of Model Context Protocol (MCP) integrations that connect internal business systems with external AI environments and assistants.
Design and implement a unified data infrastructure that consolidates information from multiple sources into a centralized layer accessible to AI agents and external tools.
Build contextual memory and customer profiling systems that ingest business data and enable increasingly personalized, intelligent AI experiences.
Establish robust reliability, observability, permission management, and durable execution mechanisms to ensure agents operate safely on real customer data.
Shape and lead the development of a next-generation AI product ecosystem for business operations, owning the architecture and technical direction of production-grade agentic systems.
Compensation
Competitive annual salary: $200,000–$300,000, depending on experience, skills, and qualifications.
Responsibilities
Lead the architecture, design, and development of a production-grade multi-agent system capable of safely executing actions across core business tools, including ERP, CRM, spreadsheets, calendars, email, analytics, and compliance platforms.
Own the development and continuous improvement of Model Context Protocol (MCP) integrations that connect internal business systems with external AI environments and assistants.
Design and implement a unified data infrastructure that consolidates information from multiple sources into a centralized layer accessible to AI agents and external tools.
Build contextual memory and customer profiling systems that ingest business data and enable increasingly personalized, intelligent AI experiences.
Establish robust reliability, observability, permission management, and durable execution mechanisms to ensure agents operate safely on real customer data.
Requirements
Nice to have: Supply chain software
Nice to have: ERP
Nice to have: CRM
Nice to have: B2B marketplaces
Nice to have: Accounting platforms
Web Solutions Engineer, Tools Automation and Infrastructure
Design, build, deploy, and improve the modern full-stack web applications using standard and Google-specific software development tools which included ensuring reliable back-ends, building pipelines to aggregate data from multiple sources, implementing responsive and User Experience (UX)-rich front-ends, or enhancing performance of existing tools and services.
Responsibilities
Design, build, deploy, and improve the modern full-stack web applications using standard and Google-specific software development tools which included ensuring reliable back-ends, building pipelines to aggregate data from multiple sources, implementing responsive and User Experience (UX)-rich front-ends, or enhancing performance of existing tools and services.
Analyze problems and develop solutions, while identifying dependencies and resolving issues to drive implementation.
Make technical contributions, including writing and reviewing design documents, tests, and code (e.g., JavaScript, Python, TypeScript, SQL).
Provide subject-matter expertise and utilize knowledge of Google's related technologies, principles, practices, and coding standards.
Collaborate with cross-functional users and stakeholders to identify pain points and devise technical solutions.
Highly skilled Full-Stack Software Engineers to contribute to an AI training project involving software development, technical problem-solving, debugging, system architecture, and code evaluation.
Compensation
Compensation is output-based. Experts are paid per task that meets the project specifications. The time required to complete each task may vary depending on the expert’s experience and workflow. Minimum submission requirements apply. Experts must submit a minimum number of tasks per week.
Responsibilities
Develop, modify, and debug front-end and back-end application components
Implement user-facing features and the services, APIs, and data models that support them
Trace requests and data across browsers, front-end applications, APIs, services, databases, and external integrations
Work inside unfamiliar repositories and understand existing application architecture
Diagnose failures involving application state, API contracts, authentication, persistence, networking, and deployment
Help shape the next generation of digital products by designing and developing scalable web applications, digital services, platform capabilities, and AI-powered solutions.
Compensation
Flexible benefits system – In addition to your salary, we provide 2700 RON/year through your benefits account, allowing reimbursements based on your preferences;
Responsibilities
Plan, develop, and operate modern web platforms, digital services, and software products
Build scalable frontend and backend applications using modern technologies and engineering practices
Develop reusable, responsive, and accessible user interfaces
Design modular, distributed, non-blocking, and event-driven system architectures
Develop AI-powered features, applications, and Agentic AI capabilities
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