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AI Solution Architect - BytePlus

ByteDance

Skills

Required

  • RAG
  • LangChain / LlamaIndex
  • LLM
  • e-commerce
  • Python
  • FastAPI
  • PyTorch / Jax
  • SQL / NoSQL
  • Spark
  • Docker
  • Kubernetes

Preferred

  • gaming

About ByteDance

  • As part of BytePlus, you'll help both enterprises and AI developers build what's next for their business.
  • Leveraging Bytedance's cutting-edge technologies in AI models, agent solutions and cloud infrastructure, we are devoted to developing innovative products and solutions to shape the future.
  • We help our clients to focus on what truly matters.
  • You can help us to achieve our mission.

Responsibilities

  • Bridge the gap between our cutting-edge Generative AI research on models and real-world industrial applications.
  • You aren't just shipping code; you are solving the "last mile" problem of AI—ensuring models are performant, grounded, and integrated into complex agent solutions to deliver results directly.
  • AI agent solution design & prototyping: Rapidly design and iterate AI agents based on clients' requirements, prototype workflows and sytem integrations, LoRA fine-tuned models tailored to specific industry needs (e.g., automated credit risk summaries for Fintech, or design style specific image/video content generation).
  • Production-ready implementation and delivery: Write production-quality code to integrate AI model APIs in various agent frameworks (ADK, LangChain, etc) with necessary tools (RAG, vectorDB, memory base, cache management, skills, etc)
  • Performance Optimization: Tackle challenges around latency, throughput, and cost. You’ll optimize prompt chains and implement caching strategies to ensure AI features scale for millions of users.
  • Evaluation & Guardrails: Develop rigorous evaluation frameworks (LLM-as-a-judge, human-in-the-loop) to ensure outputs are safe, accurate, and hallucination-free—critical for compliance-heavy industries like Finance. Also to evaluate various models on the same tasks to conclude on the pros and cons for GTM.
  • Cross-functional Collaboration: Work with product and Algorithm research teams to feed "field signals" back into the core product roadmap.

Requirements

  • AI/ML: Deep understanding of Transformer architectures, RAG, prompt engineering, and vector databases (e.g., Pinecone, Milvus, Weaviate).
  • Engineering: Proficiency in Python (FastAPI, PyTorch/Jax) and experience with orchestration frameworks like LangChain or LlamaIndex.
  • Data Systems: Comfort with SQL/NoSQL and data processing at scale (Spark, Flink).
  • Cloud/DevOps: Experience with Docker, Kubernetes, and deploying models on cloud providers.
  • Engineering Bachelor degree and above;
  • At least 2 years of related work experience in AI industry, preferably in the role of solution architect or pre-sales solution engineering
  • Product-wise, have deep understanding in AI solutions on Public Cloud, including but not limited to: LLM applications in industry verticals like e-commerce, social & entertainment, FinTech, etc
  • Hands-on experience applying Agentic AI design paradigms - Re-Act, Plan and Execute, Multi-Agent, Skills, etc.
  • AI Infra practical knowledge such as GPU selections and model inference acceleration frameworks (vLLM, sglang, etc)
  • Excellent communication skills in both English and Chinese, both written and verbal, due to necessary close and frequent collaborations with stakeholders based in Mandarin-speaking markets.

Preferred

  • Industry-wise, preferably, have worked with large enterprise clients in verticals like e-commerce, social & entertainment apps, gaming, etc, with at least over 1M daily users.
  • Comfortable with fast-paced and ambiguous "startup" environment.
  • Can-do attitude, self-starter, ability to work independently and highly motivated with contagious enthusiasm;
  • Team player and able to foster positive team morale, to set goals and drive for successful fulfilment;
  • Gives attention to details as much as to the "bigger picture"; can synthesise.