About SleekFlow

Join SleekFlow, a thriving SaaS startup that is experiencing rapid growth globally thanks to the support of renowned investors like Alibaba Entrepreneurs Fund and Tiger Global. Our mission is to develop the next generation of Conversational AI, empowering customer interactions across all channels—from messaging to voice—and enhancing business workflows and processes.

You will have the opportunity to thrive alongside the company through equity options. If you're ready to take your career to the next level and assume a larger role, we look forward to meeting you!

At SleekFlow, we have developed an all-in-one Omnichannel Conversation AI Suite that drives conversions through conversations. Our platform seamlessly integrates with popular channels such as WhatsApp, Messenger, Instagram, WeChat, Email, and more, providing comprehensive communication solutions. With our AI customer engagement platform, enterprises can automate growth by enhancing productivity across their sales, marketing, and support teams.

Our engineering culture is at the heart of our operations. When you join our team of highly skilled developers, you'll immerse yourself in an environment that fosters rapid learning and continuous professional development.

As we aggressively expand into emerging markets, we seek adventurous, diverse, and passionate individuals to join us on this exciting journey. Join our team and grow your career with SleekFlow!

Learn more from our blogs for developers: https://sleekflow.io/blog/category/developer-blog

Mid-Level

Location: Hong Kong

Minimum Experience: 1~2 years of experience working with RAG/LLM development and deployment

Language: Fluency in Chinese (Cantoness or Mandarin) and in English (reading & writing) is required, Fluency in English listening and speaking is preferable.

Role

We are seeking a highly skilled LLM/RAG/Multi-Agent Infrastructure Engineer to design, develop, and optimize infrastructure for integrating Large Language Models (LLMs) via APIs, building efficient Retrieval-Augmented Generation (RAG) systems, and enabling multi-agent frameworks. You will work at the intersection of AI applications, knowledge retrieval, and automation to create high-performance, scalable solutions that power next-generation AI-driven applications.

Key Responsibilities

• LLM Evaluation: Assess the strengths and limitations of various LLMs (e.g., OpenAI, Anthropic, Cohere) to determine the best fit for specific applications and use cases.
• RAG Pipeline Engineering: Develop and scale RAG architectures, integrating vector databases, retrieval mechanisms, and embedding models for efficient knowledge retrieval.
• Multi-Agent Coordination: Design robust infrastructure to support multi-agent interactions, enabling efficient message passing, distributed decision-making, and autonomous collaboration.
• Knowledge Base Development: Design and implement structured knowledge bases that enhance LLM output quality by integrating domain-specific data, ontologies, and automated document indexing.
• Databases & Storage: Use vector databases, NoSQL databases, and caching mechanisms (e.g., Redis, PostgreSQL, ElasticSearch) to support efficient knowledge retrieval and AI workloads.
• Observability & Monitoring: Establish logging, monitoring, and tracing systems for API performance, system health, and anomaly detection.
• Security & Compliance: Ensure best practices for data privacy, security, and compliance in AI deployments.
• Collaboration: Work closely with AI researchers, ML engineers, and software developers to integrate AI models into production environments effectively.

Required Qualifications

• Experience with LLM APIs & RAG: Hands-on experience in integrating LLM APIs (e.g., OpenAI, Anthropic, Cohere) and building RAG pipelines with vector search (e.g., FAISS, Weaviate, Pinecone).
• Multi-Agent Frameworks: Familiarity with multi-agent systems (e.g., LangChain, AutoGen, CrewAI) and their infrastructure requirements.
• Knowledge Base Engineering: Experience in designing, indexing, and maintaining structured and unstructured knowledge bases, including knowledge graph construction, ontology design, and text embedding techniques.
• CI/CD & MLOps: Experience with ML model versioning, deployment pipelines, and monitoring tools.
• Prompt Engineering Expertise: Proficiency in designing clear, structured prompts, optimizing AI responses through iterative refinement, and leveraging advanced techniques like few-shot learning and chain-of-thought reasoning for improved accuracy and coherence.

Preferred Qualifications

• Prior experience in AI infrastructure for high-traffic SaaS or enterprise AI applications.
• Knowledge of reinforcement learning and agent-based AI systems.
• Experience with real-time streaming architectures (Kafka, Ray, or similar).
• Experience in leveraging structured and semi-structured data for improving LLM-generated outputs.

What We Offer:

• Attractive compensation package, including a 13th-month salary.
• Stock options in a rapidly growing startup.
• A fun, diverse, and international team culture.
• Comprehensive group medical insurance.
• Transport allowance.
• Examination & education allowance.
• Paid birthday leave.
• Flexible work-from-home policy.
• “Work From Anywhere” scheme.
• Rewarding continuous learning from experienced team leads.
• Many more exciting perks and growth opportunities!

Confidence can sometimes hold us back from applying for a job. But we'll let you in on a secret: there's no such thing as a 'perfect' candidate.

SleekFlow is a place where everyone can grow. So however you identify and whatever background you bring with you, please apply if this is a role that would make you excited to come into work every day.

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