As an AI Solutions Consultant, you will drive transformative change by designing, building, and deploying innovative AI-powered solutions across domains such as Generative AI, Anti-Fraud, Agent Systems, and Content & Growth Automation. You'll work hands-on to develop scalable, production-grade applications while acting as a trusted advisor to internal stakeholders, translating complex business and technical challenges into high-impact outcomes.
This role involves close collaboration with business leaders, product owners, and engineering teams to understand organizational needs and then leverage your expertise in LLMs, multi-agent orchestration, graph reasoning, or anomaly detection to develop tailored, production-ready solutions grounded in real-world context.
Providing hands-on consulting and technical expertise across multiple use cases - from customer support automation to fraud risk detection and AI content workflows - will be central to your role. You'll also work with platform and infrastructure teams to ensure AI solutions are robust, reproducible, and scalable.
Key Responsibilities
Solution Architecting
- Work with business and technical leads to manage and deliver successful implementations of AI-powered solutions across multiple verticals (e.g., GenAI copilots, fraud prevention pipelines, agent orchestration, content automation).
- Propose solution architectures and manage deployment strategies according to complex organizational requirements and best practices.
- Translate business objectives into actionable AI-powered use cases by gathering requirements, auditing data readiness, and designing integration flows for models, tools, and systems.
- Interact with internal stakeholders to manage project scope, priorities, deliverables, risks, and timelines for successful AI solution rollout.
Solution Engineering
- Develop, test, and deploy production-ready AI applications that integrate LLMs, retrieval pipelines, multi-agent logic, or real-time anomaly detection algorithms.
- Write clean, efficient code while optimizing for performance, cost, and latency across cloud and hybrid environments.
- Continuously evaluate and improve the performance, scalability, and efficiency of deployed solutions by incorporating new techniques and tooling.
Education & Enablement
- Collaborate with internal platform, product, and data teams to feed insights and learnings into roadmap design and reusable asset creation.
- Package successful approaches and best practices into internal methodologies, templates, and frameworks for broader enablement.
- Share your expertise through workshops, internal training sessions, and documentation to scale AI capabilities across the organization.
- Stay up-to-date with the rapidly evolving GenAI and applied AI landscape, proactively tracking emerging technologies in fields like large language models, graph ML, or adversarial behavior modeling.
Required Qualifications
Enterprise Application Development
- 3+ years of experience designing and developing enterprise-class applications, with a solid grasp of software development lifecycle principles.
AI/LLM Proficiency
- 1+ years of hands-on experience with Large Language Models (LLMs), including prompt engineering, fine-tuning, vector store integration, and application design.
- Familiarity with foundation models across providers (OpenAI, Claude, Gemini, Qwen, Mistral, etc.) and open-source ecosystems.
Programming & Infrastructure
- Proficiency in at least one major programming language (Python, JavaScript, Java, or C#).
- Experience with Git-based version control and deployment frameworks using Docker, Kubernetes, or serverless tools.
Cloud & AI Ecosystem
- Experience deploying AI workloads on AWS, Azure, GCP, or Aliyun cloud platforms.
- Familiarity with modern AI frameworks and orchestration tools (e.g., LangChain, LlamaIndex, Haystack, LangGraph).
Domain & Data Experience
- Strong foundation in one or more of the following applied AI domains:
- Anti-Fraud: Behavioral modeling, risk scoring systems, graph-based fraud detection, or transaction anomaly detection.
- Agent Systems: LLM-powered autonomous agents, multi-turn interaction design, decision orchestration using tool/function calling.
- Content & Growth Automation: Scalable content generation, AI-driven SEO optimization, personalized growth strategies using data and model-driven insights.
- Understanding of data pipelines and experience with relational and non-relational databases (e.g., SQL, NoSQL, vector DBs).
Soft Skills
- Excellent communication and interpersonal skills to align cross-functional stakeholders.
- Strong analytical and problem-solving abilities to address ambiguous challenges and drive structured innovation.
- Demonstrated ability to engage, influence, and align with cross-functional business stakeholders across departments (e.g. operations, marketing, risk, product).
- Able to use Chinese and English as working language to work with chinese speaking stakeholders
Preferred Qualifications
- A background in management consulting, strategy, or enterprise solution delivery is strongly preferred, reflecting the ability to synthesize complex business needs into actionable AI initiatives.
- Experience in user-facing systems where latency, trust, and explainability are critical.
