Solutions Architect Graduate (BytePlus, Singapore) - 2027 Start

Team Introduction Join the Innovative BytePlus Team! As part of BytePlus, you will help enterprises and AI-native developers build the next generation of their businesses. By leveraging ByteDance’s cutting-edge technologies in AI models, agent solutions, and cloud infrastructure, we are committed to developing innovative products and solutions that shape the future. We enable our clients to focus on what matters most, and you can play a key role in helping us achieve that mission.

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume. Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.

Responsibilities

• Bridge the gap between cutting-edge Generative AI model research and real-world industrial applications by solving the “last mile” of AI deployment, ensuring models are performant, grounded, and effectively integrated into complex agent solutions that deliver direct business impact.
• Design and prototype AI-native agent solutions based on client requirements, including agent workflows, system integrations, and LoRA fine-tuned models tailored to specific industry use cases, such as automated credit risk summaries for fintech or style-specific image and video generation.
• Deliver production-ready implementations by writing high-quality code to integrate AI model APIs into agent frameworks such as ADK and LangChain, together with supporting components such as RAG, vector databases, memory systems, cache management, and skills.
• Optimize system performance across latency, throughput, and cost dimensions by improving prompt chains and implementing scalable caching strategies for AI features serving large user bases.
• Develop evaluation frameworks and guardrails, including LLM-as-a-judge and human-in-the-loop approaches, to ensure outputs are safe, accurate, and reliable, especially for compliance-sensitive industries such as finance. Evaluate multiple models on common tasks to identify trade-offs and inform go-to-market decisions.
• Collaborate closely with product and algorithm research teams to feed real-world customer signals back into the product roadmap and model development process.

Requirements

Minimum Qualification(s)

• Individuals who are completing or have recently completed a Bachelor’s or Master’s degree in Engineering or a related discipline.
• Understanding of AI solutions on public cloud platforms, including LLM applications across industry verticals such as e-commerce, social and entertainment, fintech, education, and enterprise services.
• Familiarity with agentic AI design paradigms such as ReAct, Plan-and-Execute, Multi-Agent, and Skills-based systems.
• Hands-on experience with AI/ML projects involving agent frameworks and technical areas such as PageIndex, RAG, prompt engineering, and context engineering.
• Proficiency in Python, including frameworks such as FastAPI and deep learning stacks such as PyTorch or JAX, as well as experience with orchestration frameworks like LangChain or LlamaIndex.
• Experience with Docker, Kubernetes, and deploying models on cloud platforms.

Preferred Qualification(s)

• Prior academic, research, or internship project experience in AI-native domains; internship experience in roles such as Solution Architect, Forward Deployed Engineer, or AI Agent Product/Engineer is a plus.
• Strong communication skills.
• Proactive, self-motivated, and able to work independently with a strong sense of ownership.