Pioneer Talent Program - AI Productivity Engineer
Thông tin thêm
Loại hình
full time
Kinh nghiệm
mid
Phòng ban
Backend
6 việc tương tự đang tuyển
Mô tả công việc
About the Role
As AI capabilities rapidly evolve, we are building a unified AI-powered productivity platform to transform how engineering work gets done across the organization.
In this role, you will design and deliver AI tools, systems, and workflows that augment developers, streamline processes, and significantly boost engineering efficiency and quality. You will work at the intersection of AI and software engineering, turning cutting-edge AI capabilities into practical, high-impact internal solutions.
What You Will Do
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Design and build internal AI productivity tools (e.g., coding assistants, knowledge retrieval systems, workflow automation tools)
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Develop LLM-powered applications for engineering use cases such as code generation, code understanding, and test generation
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Integrate AI capabilities into the end-to-end software development lifecycle (from requirements to deployment)
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Partner with engineering teams to identify bottlenecks and deliver AI-driven solutions
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Experiment with and implement techniques such as Prompt Engineering, RAG (Retrieval-Augmented Generation), and AI Agents
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Continuously evaluate and adopt emerging AI technologies to enhance internal platforms
What We're Looking For
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Bachelor’s degree or above in Computer Science or a related field
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Strong programming skills in at least one language (e.g., Python, Java, Go, JavaScript)
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Solid understanding of data structures and algorithms
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Basic understanding of large language models (LLMs) and their practical applications
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Experience using AI tools such as ChatGPT, Copilot, or similar
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Familiarity with backend or web development fundamentals
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Understanding of APIs and system integration
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Strong problem-solving skills and a proactive, ownership-driven mindset
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Good communication and collaboration skills
Bonus Points
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Hands-on experience with Prompt Engineering, RAG, or AI Agents
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Experience building AI tools, developer tools, or internal platforms
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Exposure to DevOps, CI/CD, or engineering automation
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Experience with knowledge systems, search systems, or documentation platforms
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Contributions to open-source projects or active GitHub portfolio
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