AI Developer
Role details
Job location
Tech stack
Job description
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Design, develop, and enhance AI-powered features for the CHQ platform and Tulip AI ecosystem.
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Build user-centric workflows, productivity tools, and intelligent automation capabilities for corporate users.
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Integrate frontend applications with backend APIs, AI services, and enterprise systems.
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Develop AI-assisted user experiences including conversational workflows, recommendations, and intelligent task support.
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Work with Generative AI services, LLM APIs, semantic search, and workflow orchestration platforms.
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Collaborate with backend and platform teams to integrate APIs, authentication, and enterprise data sources.
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Implement scalable, reusable, and maintainable application components and feature modules.
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Support enterprise workflow automation and operational productivity initiatives.
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Ensure application responsiveness, usability, performance, and reliability.
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Participate in Agile development processes including sprint planning, feature estimation, and technical discussions.
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Write clean, testable, and maintainable code following engineering best practices.
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Support debugging, monitoring, testing, and production issue resolution activities.
Requirements
Need to Have
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4+ years of experience in application development or feature engineering.
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Strong experience in frontend or full-stack application development.
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Experience integrating AI services, LLM APIs, or Generative AI-powered features.
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Strong understanding of REST APIs, frontend-backend integration, and asynchronous workflows.
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Experience working with modern frontend frameworks such as React, Angular, or similar technologies.
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Familiarity with enterprise application architecture and workflow-based platforms.
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Knowledge of authentication, authorization, and secure application development practices.
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Experience with Agile development methodologies and Git-based workflows.
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Strong problem-solving, debugging, and communication skills.
Good to Have
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Experience with enterprise productivity platforms or internal business applications.
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Exposure to conversational AI, workflow automation, or AI assistant platforms.
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Familiarity with Python, FastAPI, or backend service integrations.
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Experience with semantic search, RAG architectures, or vector databases.
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Exposure to cloud platforms such as AWS, Azure, or Google Cloud Platform.
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Experience in QSR, retail, hospitality, or large-scale enterprise environments.
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Knowledge of analytics, monitoring, and observability tools.
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Experience collaborating in co-development or distributed engineering models.