Binance
Senior AI Engineer Engineering Productivity
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3h ago
Senior AI Engineer (Engineering Productivity)
Asia / Hong Kong
Engineering – Data Science/AI /
Full-time Onsite or Remote /
Remote
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We're looking for a Senior AI Engineer to join our Engineering Productivity team, focused on leveraging AI/LLM technologies to fundamentally improve how our engineers build, test, deploy, and operate software. You'll design and implement AI-powered tools and systems that measurably accelerate development velocity across the organization.
What You'll Do:
Design and build AI-powered developer tools (code generation, automated review, intelligent testing) that improve engineering efficiency across the company
Develop and deploy LLM-based applications (RAG, AI Agents, code understanding) to streamline internal workflows — from coding to deployment to incident response
Build and optimize AI-assisted CI/CD pipelines, including intelligent test selection, build failure prediction, and deployment risk assessment
Create internal knowledge retrieval systems (RAG-based) enabling engineers to quickly search and understand codebases, documentation, and architectural decisions
Evaluate, fine-tune, and integrate state-of-the-art LLMs (open-source and commercial) for engineering use cases
Define metrics and conduct A/B tests to measure the impact of AI tools on developer productivity
Collaborate closely with engineering teams across the organization to identify high-impact automation opportunities
What We're Looking For:
5+ years of software engineering experience, with at least 2 years focused on AI/ML engineering
Strong hands-on experience with LLMs — including prompt engineering, RAG, fine-tuning (SFT/RLHF), and AI Agent frameworks (LangChain, LangGraph, CrewAI, or similar)
Solid programming skills in Python; experience with Go, Java, or TypeScript is a plus
Experience building production-grade AI applications — not just prototypes
Familiarity with code analysis tools and techniques (AST parsing, static analysis, LSP) is highly preferred
Understanding of modern CI/CD systems and DevOps practices
Strong system design skills — ability to architect scalable, reliable services
Excellent problem-solving skills with a pragmatic, results-oriented mindset
Nice to Have:
Experience building AI coding assistants or developer productivity tools
Experience with code embedding models and code search systems
Contributions to open-source AI/ML projects
Publications in relevant areas (NLP, code intelligence, SE + AI)
Experience in the Web3/crypto industry
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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Binance
Location: Hong Kong
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Requirements
5+ years of software engineering experience, with at least 2 years focused on AI/ML engineering Strong hands-on experience with LLMs — including prompt engineering, RAG, fine-tuning (SFT/RLHF), and AI Agent frameworks (LangChain, LangGraph, CrewAI, or similar) Solid programming skills in Python; experience with Go, Java, or TypeScript is a plus Experience building production-grade AI applications — not just prototypes Familiarity with code analysis tools and techniques (AST parsing, static analysis, LSP) is highly preferred Understanding of modern CI/CD systems and DevOps practices Strong system design skills — ability to architect scalable, reliable services Excellent problem-solving skills with a pragmatic, results-oriented mindset
Responsibilities
Design and build AI-powered developer tools (code generation, automated review, intelligent testing) that improve engineering efficiency across the company Develop and deploy LLM-based applications (RAG, AI Agents, code understanding) to streamline internal workflows — from coding to deployment to incident response Build and optimize AI-assisted CI/CD pipelines, including intelligent test selection, build failure prediction, and deployment risk assessment Create internal knowledge retrieval systems (RAG-based) enabling engineers to quickly search and understand codebases, documentation, and architectural decisions Evaluate, fine-tune, and integrate state-of-the-art LLMs (open-source and commercial) for engineering use cases Define metrics and conduct A/B tests to measure the impact of AI tools on developer productivity Collaborate closely with engineering teams across the organization to identify high-impact automation opportunities