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Career International AP (Hong Kong) Limitedvia Indeed
AI Engineer
HKPosted 2mo ago
ML EngineerMid LevelFull-time#pytorch#tensorflow#huggingface#langchain#llm#transformers#aws#gcp#azure
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About the Role
Key responsibility:
Build and deploy end‑to‑end AI solutions using RAG, agent frameworks, and MCP servers, linking LLMs with internal systems through APIs from major AI providers.
Work closely with investment and business teams to surface impactful use cases, convert requirements into technical workflows, and deliver training, prompts, and clear documentation.
Develop structured evaluation methods to compare external AI tools, agents, and models across investment and financial datasets, ensuring strong performance and reliability.
Stand up secure, scalable AI/LLM environments on cloud platforms (AWS, Azure, GCP, Snowflake, Databricks) to address business needs quickly.
Test and implement new AI technologies
Qualifications & Experience
8+ years in applied AI, solution engineering, or technical product work.
Degree in Computer Science, Engineering, Applied Mathematics, or related field.
Strong grasp of the modern AI ecosystem: transformers, agent workflows, context/prompt design, model evaluations, and LLM orchestration.
Hands‑on experience adapting and integrating GenAI tools (e.g., ChatGPT, Claude, Gemini, Perplexity, DeepSeek, GitHub Copilot, Cursor) into real business pipelines.
Language Model Expertise (Preferred) : Experience building, fine-tuning, or deploying LLMs for production use. Familiarity with frameworks and tools such as Hugging Face Transformers, LangChain, LangGraph, PyTorch, TensorFlow, and OpenAI APIs .
Proven track record in cloud deployment on GCP , with large-scale financial project experience (highly preferred).
Experience in financial services, hedge funds, asset management, or fintech, is beneficial.
Professional fluency in English; Mandarin is advantageous.
Full-time
Build and deploy end‑to‑end AI solutions using RAG, agent frameworks, and MCP servers, linking LLMs with internal systems through APIs from major AI providers.
Work closely with investment and business teams to surface impactful use cases, convert requirements into technical workflows, and deliver training, prompts, and clear documentation.
Develop structured evaluation methods to compare external AI tools, agents, and models across investment and financial datasets, ensuring strong performance and reliability.
Stand up secure, scalable AI/LLM environments on cloud platforms (AWS, Azure, GCP, Snowflake, Databricks) to address business needs quickly.
Test and implement new AI technologies
Qualifications & Experience
8+ years in applied AI, solution engineering, or technical product work.
Degree in Computer Science, Engineering, Applied Mathematics, or related field.
Strong grasp of the modern AI ecosystem: transformers, agent workflows, context/prompt design, model evaluations, and LLM orchestration.
Hands‑on experience adapting and integrating GenAI tools (e.g., ChatGPT, Claude, Gemini, Perplexity, DeepSeek, GitHub Copilot, Cursor) into real business pipelines.
Language Model Expertise (Preferred) : Experience building, fine-tuning, or deploying LLMs for production use. Familiarity with frameworks and tools such as Hugging Face Transformers, LangChain, LangGraph, PyTorch, TensorFlow, and OpenAI APIs .
Proven track record in cloud deployment on GCP , with large-scale financial project experience (highly preferred).
Experience in financial services, hedge funds, asset management, or fintech, is beneficial.
Professional fluency in English; Mandarin is advantageous.
Full-time
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