Application Development & AI Integration:
- Lead the technical architecture and development of applications, incorporating generative AI features where beneficial
- Design and implement robust AI pipelines for text, image, and code generation capabilities
- Develop and maintain prompt engineering frameworks and best practices
- Oversee the integration of multiple AI models and APIs while ensuring optimal performance and cost efficiency
- Establish technical standards for responsible AI development, including bias detection and mitigation strategies
Requirements:
Educational Background:
- Degree in Computer Science, Software Engineering, Artificial Intelligence, or related technical field
- Professional certifications in AI/ML technologies are advantageous
Technical Expertise:
- 5+ years of experience in application development and technical team management
- Minimum 2 years of hands-on experience with generative AI technologies
- Demonstrated expertise in:
- Large Language Models (LLMs) and their applications
- Prompt engineering and chain-of-thought implementations
- Vector databases and embedding technologies
- AI model fine-tuning and deployment
- RAG (Retrieval-Augmented Generation) architectures
- Strong background in software architecture and system design
- Proficiency in Python and modern development frameworks
- Experience with AI/ML platforms (e.g., OpenAI, Anthropic, Hugging Face)
- Knowledge of cloud platforms (AWS, GCP, Azure) and containerisation technologies
- Understanding of API design, microservices architecture, and system integration
- Leadership & Professional Skills:
- Proven track record of leading technical teams and managing complex AI projects
- Strong problem-solving abilities and analytical thinking
- Experience in AI governance and ethical considerations
- Excellent communication skills and ability to work with diverse stakeholders
- Experience in agile methodologies and MLOps practices
- Understanding of government digital services is preferred
Additional AI Experience:
- Experience with multimodal AI systems (text, image, audio)
- Knowledge of AI safety and security best practices
- Familiarity with AI model evaluation metrics and performance optimization
- Understanding of AI infrastructure scaling and cost management
- Experience with AI-specific testing and quality assurance methodologies
