Senior AI Engineer

About the Role

Responsible for the complete AI development lifecycle from ideation, model/tool selection, implementation, testing and roll out including monitoring and optimization.

Responsibilities

• End-to-End AI development: ideation, model/tool selection, implementation, testing, roll out, monitoring and optimization.
• Model optimization: fine-tune and optimize large-scale generative models (e.g., LLMs) using techniques like RLHF.
• Prompt engineering: design and manage advanced prompts to control and improve model output quality and safety.
• Data curation: curate high-quality datasets for model fine-tuning and building knowledge bases.
• RAG system development: build and maintain Retrieval-Augmented Generation pipelines, including vector databases.
• Deployment & API integration: deploy models and develop scalable APIs for integration into applications.
• Evaluation & safeguarding: evaluate model performance and implement safeguards against hallucination and bias.
• Project coordination & communication: communicate plans, status, risks and issues to project governance and stakeholders; serve as single point of contact for assigned applications; provide training and system specification documents to peers and users where appropriate.
• Technical support & improvement: investigate and resolve application functionality issues, provide 1st and 2nd level support, troubleshooting and identify required modifications; learn new technologies, languages and techniques to adapt to evolving business needs.

Qualifications

• Bachelor’s degree in AI, Data Science, Computer Science, Engineering, IT or a related field.
• Creative individual with deep technological expertise in AI development and cloud background.
• Good command in English (Minimum 750 TOEIC score).
• Goal-Oriented, Unity, Learning, Flexible.

Technical Skills

• Expert-level Python programming skills.
• Extensive hands-on experience with LLM frameworks, e.g. LangChain, for building complex LLM-powered applications including agentic workflows and chains.
• Microsoft Azure: Azure OpenAI Service (deploying, fine-tuning and managing foundational models), Azure AI Search for RAG solutions, Azure AI Studio, and Copilot integration experience.
• Google Cloud: Vertex AI Platform (managing generative AI lifecycle), Vertex AI Search and Conversation for RAG-based search and conversational applications, Vertex AI Model Garden & Generative AI Studio, and Vertex AI Pipelines for orchestrating GenAI workflows.