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Designs and maintains scalable data pipelines, platform infrastructure, and backend services supporting AI Agent systems. Builds monitoring, alerting, observability, and data-quality capabilities while integrating distributed systems, databases, and message queues. Collaborates with AI, product, DevOps, frontend, and QA teams, and supports existing services to ensure stability and continuity.
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’re looking for a hands-on backend engineer to build AI Agent services for personalized market briefings and news. You’ll develop Golang services, design data flows, and bring Agent workflows into production, with a focus on reliability, scale, and cost efficiency.
Responsibilities
- Build and maintain Golang services, APIs, and Agent workflows for AI-generated market briefings and news updates.
- Design data flows, data models, and service architecture to support content generation and distribution.
- Build high-concurrency, highly available systems for personalized content delivery, including scheduled pushes that must complete within tight windows—for example, within 10 minutes.
- Monitor data quality, Agent workflow health, generation latency, and AI costs. Improve performance and reliability while keeping inference costs low.
- Own existing and new services through development, deployment, maintenance, and troubleshooting, working closely with frontend, AI, data, DevOps, and QA teams.
Requirements
- Strong backend engineering experience, ideally 3–10 years, with recent hands-on production development in Golang. Relevant delivery experience matters more than total years.
- Proven experience building and operating AI Agents in production, including workflow orchestration, tool/API integration, failure handling, and monitoring.
- Practical experience in data engineering and data architecture, including data pipelines, data modeling, and backend data processing.
- Solid understanding of distributed systems, microservices, high concurrency, and high-availability design.
- Experience with relational and NoSQL databases, and message queues such as Kafka.
- Strong ownership, clear communication, and the ability to work effectively in a fast-paced, product-driven team.
- Bachelor’s degree or above in Computer Science, Engineering, or a related field.
- Experience with AI Agent frameworks, reusable AI tooling, or observability platforms.
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.
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Binance Singapore, Singapore, SGP Office
Singapore, Singapore
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.
