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Senior AI Engineer Engineering Productivity

NEW
Hong Kong, Hong KongFull-timeGlobal
📊 Junior🏠 Onsite
ActivePosted within the last 30 days

Job Description

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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

Tech Stack

aiengineerseniorjava
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