Ambition Hiring! Full Time Generative AI Engineer in Federal Territory - Ricebowl

Our client is a leading regional financial institution that is actively investing in next-generation AI capabilities to drive digital innovation and intelligent automation across the organisation. As part of their growing AI & Emerging Technology team, they are looking for a Gen AI Engineer to design, develop, and scale enterprise-grade Generative AI solutions that will shape the future of banking experiences and internal productivity.

• Develop and deploy advanced AI models leveraging the latest advancements in Large Language Models (LLMs), transformers, and Generative AI technologies for enterprise applications and chatbot solutions.
• Design, optimize, and enhance AI systems to improve model efficiency, scalability, accuracy, and overall performance across production environments.
• Collaborate closely with data engineers, architects, and technology stakeholders to implement effective data pipelines and model training strategies.
• Build and maintain AI infrastructure including RAG pipelines, vector embeddings, prompt engineering frameworks, and model orchestration workflows.
• Conduct testing, validation, and continuous improvement of AI models to ensure reliability, security, and compliance with enterprise quality standards.
• Contribute to engineering best practices through clean coding, technical documentation, peer reviews, and active knowledge sharing within the team.

• Bachelor's Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or related technical disciplines. Master's Degree or advanced specialization in AI/ML-related fields would be an added advantage.
• Minimum 2-3 years of experience in AI Engineering, Machine Learning Engineering, Data Science, or Generative AI-related development.
• Strong hands-on experience in building and deploying LLMs, transformers, and enterprise-grade AI applications.
• Good exposure to RAG pipelines, vector databases/embeddings, tokenization, prompt engineering, fine-tuning, zero-shot and few-shot learning techniques.
• Proficiency in Python and strong experience with AI/ML frameworks such as TensorFlow, PyTorch, and Hugging Face ecosystem.
• Familiarity with cloud and hybrid deployment environments including AWS, Azure, or Google Cloud, with understanding of scalability and security considerations for AI systems.
• Strong problem-solving mindset, communication skills, and ability to work collaboratively within fast-paced engineering environments.