Develop, deploy, and maintain LLM pipelines and RAG QA/search systems; design and optimize prompts and multi-agent LLM architectures; operate multi‑GPU/cluster inference; build evaluation pipelines for model quality, bias, and hallucination; collaborate with product and CS teams to integrate conversational AI.
Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. We are trusted by 300+ million people in 100+ countries for our industry-leading security, user fund transparency, trading engine speed, deep liquidity, and an unmatched portfolio of digital-asset products. Binance offerings range from trading and finance to education, research, payments, institutional services, Web3 features, and more. We leverage the power of digital assets and blockchain to build an inclusive financial ecosystem to advance the freedom of money and improve financial access for people around the world.
We are seeking a highly skilled professional to join our team, focusing on advancing through innovative AI solutions.
The successful candidate will develop and refine Large Language Models (LLMs) to extract actionable insights, improve business decision-making, and optimize prompt design for more accurate outputs. Additionally, the role includes creating scalable and robust LLM/RAG frameworks tailored to customer service scheduling, fostering innovation and maintaining a competitive market edge.
This role is 100% Remote, Work from Home based.
Responsibilities
- Own the full LLM pipeline from data preparation to production real case usage.
- Design, iterate and optimize prompts (zero-/few-shot, chain-of-thought, tool-calling, etc.) to maximize model utility and safety across products and languages.
- Build and maintain Retrieval-Augmented Generation (RAG) QA/search systems that connect to multi-source knowledge bases.
- Familiar with vLLM/SGLang inference architectures and have proven experience deploying and operating LLM services on multi‑GPU or cluster environments.
- Design, implement and operate multi‑agent LLM architectures (e.g. LangGraph, CrewAI, AutoGen) including task decomposition, agent orchestration, memory sharing and tool‑calling workflows.
- Develop evaluation pipelines (automatic metrics & human feedback) to measure prompt and model quality, bias, and hallucination rates.
- Collaborate with product and CS teams to integrate AI models into conversational Chatbot in different scenarios.
- Track cutting-edge research, author tech blogs, and keep improve current architecture.
Requirements
- Master’s Degree or higher in Computer Science, Data Science or related field..
- At least 2 years of deep-learning/NLP experience, including 1+ year practical LLM work (SFT, DPO, RAG, quantization, inference optimization, etc.).
- Demonstrated prompt engineering & tuning expertise (few-shot design, structured prompting, prefix-/p-tuning, reward re-ranking, safety filtering).
- Practical experience building and deploying multi‑agent LLM workflows, with understanding of agent‑orchestrator patterns, shared memory, long‑horizon planning and guard‑rail design.
- Proficient in both English and Chinese communication for efficient cross team collaboration
Why Binance
• Shape the future with the world’s leading blockchain ecosystem
• Collaborate with world-class talent in a user-centric global organization with a flat structure
• Tackle unique, fast-paced projects with autonomy in an innovative environment
• Thrive in a results-driven workplace with opportunities for career growth and continuous learning
• Competitive salary and company benefits
• Work-from-home arrangement (the arrangement may vary depending on the work nature of the business team)
Binance is committed to being an equal opportunity employer. We believe that having a diverse workforce is fundamental to our success.
By submitting a job application, you confirm that you have read and agree to our Candidate Privacy Notice.
Binance Singapore, Singapore, SGP Office
Singapore, Singapore
Similar Jobs
Artificial Intelligence • Consumer Web • Digital Media • Information Technology • Social Impact • Software
Lead the AI Quality engineering team while remaining hands-on building evaluation infrastructure, observability tooling, diagnostic systems, datasets, annotation workflows, and CI/CD pipelines for production AI agents. Run experiments across prompts, models, routing, caching, and agent harnesses to improve quality, latency, and cost. Partner with AI Core engineering and manage technical direction and team priorities.
Top Skills:
Ai AgentsBraintrustCi/CdLangsmithLlmsPythonRuby On Rails
Artificial Intelligence • Marketing Tech • Sales • Software
Lead and scale the engineering organization for a regulated digital-asset custody platform. Own hiring, org design, engineering operations (on-call, incidents, release hygiene), delivery against roadmaps, audit and regulator readiness (SOC 2, SAMA, ISO 27001), and cross-functional alignment. Amplify an existing small, specialized team to meet regulatory and institutional requirements while preserving culture and retention.
Top Skills:
Aurora PostgresAWSBitcoinClickhouseEthereumGithub ActionsGoHsmKubernetesMpc/TssRustSolanaTemporalTerraformThreshold Signing ProtocolsTypescript
Professional Services • Real Estate • Consulting
Manage cost management services for infrastructure construction projects across pre- and post-contract stages. Responsibilities include estimating, cost planning, tendering, procurement, contract documentation, cost reporting, variance management, change control, value engineering, client presentations, consultant coordination, process improvement, knowledge management, and supporting junior cost managers.
Top Skills:
Cost-X
What you need to know about the Singapore Tech Scene
The digital revolution has driven a constant demand for tech professionals across industries like software development, data analytics and cybersecurity. In Singapore, one of the largest cities in Southeast Asia, the demand for tech talent is so high that the government continues to invest millions into programs designed to develop a talent pipeline directly from universities while also scaling efforts in pre-employment training and mid-career upskilling to expand and elevate its workforce.
