AI Engineer

Responsibilities Build and deploy end‑to‑end GenAI 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 technologiesQualifications & Experience5+ 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.Skilled in Python, SQL, and GitHub, with working knowledge of major cloud/data platforms (AWS, Azure, GCP, Snowflake, Databricks).Experience in financial services, hedge funds, asset management, or fintech, is beneficial.Professional fluency in English; Mandarin is advantageous.Desired TraitsSelf‑driven, proactive, and comfortable working independently.Detail‑oriented, results‑focused, and reliable under pressure.Collaborative team contributor with strong work ethic and maturity.Able to translate complex technical concepts into simple explanations for non‑technical audiences.