Fulcrum

• Own the product backlog: creation, grooming, prioritization, and refinement • Gather requirements through stakeholder interviews, workshops, and research • Write structured deliverables: user stories, acceptance criteria, PRDs, process diagrams • Plan and manage sprints, timelines, and delivery milestones • Track project health: risks, blockers, scope changes, and resource allocation • Communicate project status and expectations to stakeholders and leadership • Collaborate daily with designers, developers, and QA to clarify and validate requirements • Review built functionality against requirements and adjust documentation accordingly • Continuously bridge business goals and product/engineering decisions

• Develop a desktop/overlay solution for in-game user experience • Build fullstack functionality (frontend + backend) • Integrate with games via save files, APIs, mods, and other data sources • Work with AI features (RAG, contextual hints, intelligent responses) • Contribute to MVP development and rapid hypothesis validation

• Develop a desktop/overlay solution for in-game user experience • Integrate with games via save files, APIs, mods, and other data sources • Work with AI features (RAG, contextual hints, intelligent responses) • Contribute to MVP development and rapid hypothesis validation

• monitor overall health of AI projects (velocity, quality, blockers, timelines) and intervene early when needed • review and approve ключові технічні deliverables (architecture, APIs, security, DB, deployment) • define and enforce engineering processes (code reviews, CI/CD, testing, documentation, AI tooling) • approve presale development plans (architecture, stack, team, estimates) • approve discovery setup (processes, cadence, risks, tooling, acceptance criteria) • drive adoption of AI dev tools and measure impact (Claude, Codex, agents) • evaluate new AI requirements (feasibility, effort, risks, approach) • identify and внедряти AI-driven process improvements (automation, testing, monitoring, docs) • oversee custom agent development (quality, value, best practices) • track and evaluate new AI/ML technologies, maintain internal knowledge base • lead internal AI education (workshops, materials, onboarding) • support engineer growth via feedback and skill gap identification • participate in hiring and technical assessments for AI roles • run mentorship initiatives for AI skill development

• Own the product backlog: creation, grooming, prioritization, and refinement • Gather requirements through stakeholder interviews, workshops, and research • Write structured deliverables: user stories, acceptance criteria, PRDs, process diagrams • Plan and manage sprints, timelines, and delivery milestones • Track project health: risks, blockers, scope changes, and resource allocation • Communicate project status and expectations to stakeholders and leadership • Collaborate daily with designers, developers, and QA to clarify and validate requirements • Review built functionality against requirements and adjust documentation accordingly • Continuously bridge business goals and product/engineering decisions

• Collect, analyze, and document business and functional requirements • Create and maintain project documentation: Vision, Scope, User Stories, Acceptance Criteria, Use Cases, and User Flows • Plan and oversee project execution in collaboration with designers, developers, and QA engineers • Define, maintain, and prioritize the product backlog • Lead client meetings, demos, retrospectives, and workshops • Communicate effectively with internal and external stakeholders • Ensure product delivery aligns with client business goals • Analyze results, gather feedback, and suggest improvements to enhance the product • Manage scope, time and budget

• Develop new features • Support & bug fixing

• Contribute to the development of our web application with a focus on responsive, high-quality UI • Implement features in collaboration with product managers, designers, and backend engineers • Use design-to-code tools (such as Figma-to-code pipelines) to translate designs into production-ready components • Build and maintain reusable frontend components and services integrated with backend APIs • Integrate and orchestrate AI coding agents for tasks like automated code review, test generation, refactoring, and documentation • Stay current with the fast-moving AI tooling ecosystem and proactively introduce tools and workflows that raise the team's velocity and output quality • Ensure high standards of code quality, testing, and performance optimization • Help shape frontend architecture for scalability, maintainability, and performance • Communicate effectively in spoken and written English

• Design and develop core product functionality for an AI-powered platform with autonomous agents • Build and improve the agent runtime that allows AI agents to execute tasks, interact with tools, and run in isolated environments • Develop chat-based and conversational interfaces that enable users to interact naturally with their AI agents • Implement and evolve the memory system that allows agents to learn from conversations and store long-term context • Design and build APIs and backend services powering agents, integrations, and real-time features • Work with vector databases and RAG pipelines to support intelligent retrieval and context building • Build real-time experiences (streaming responses, live updates) for interactive agent execution • Participate in building the multi-agent orchestration layer, enabling collaboration between multiple AI agents • Contribute to the AI tool ecosystem, integrating external services and building extensible agent skills • Collaborate with product, design, and engineering teams to iterate quickly and deliver new features

• Design and develop core product functionality for an AI-powered platform with autonomous agents • Build and improve the agent runtime that allows AI agents to execute tasks, interact with tools, and run in isolated environments • Develop chat-based and conversational interfaces that enable users to interact naturally with their AI agents • Implement and evolve the memory system that allows agents to learn from conversations and store long-term context • Design and build APIs and backend services powering agents, integrations, and real-time features • Work with vector databases and RAG pipelines to support intelligent retrieval and context building • Build real-time experiences (streaming responses, live updates) for interactive agent execution • Participate in building the multi-agent orchestration layer, enabling collaboration between multiple AI agents • Contribute to the AI tool ecosystem, integrating external services and building extensible agent skills • Collaborate with product, design, and engineering teams to iterate quickly and deliver new features