Head of Ai

Digital Blue Foam is seeking an Head of AI to help us build our interactive design software for Architecture Engineering and Construction (AEC) Industry.

Digital Blue Foam (DBF) is a Singapore-based technology company developing tools to accelerate the world’s transition to more sustainable, livable, and equitable cities. Our core product is a tool to help different stake-holders hunt and gather web-based information, generate design options, and automate BIM workflows directly in the browser. For designers, architects, and developers, who often need to work together to make a high-rise project a reality, DBF will save considerable time and lead to superior results.

Digital Blue Foam is seeking an **Head of AI** to help us build our interactive design software for Architecture Engineering and Construction (AEC) Industry.

This role will be responsible for:

- AI Roadmap vis-a-vis Business vision/goals/initiatives (VGIs)
- Ideas & insights into newer AI-powered business models
- Projects/products implementation methodologies/processes (agile etc.)
- AI algorithms, research & related roadmap
- Technologies & related roadmap
- AI platform/products architecture/design/implementation vis-a-vis cloud AI services
- AI infrastructure vis-a-vis cloud AI services
- AI automation (pipeline) projects
- AI/ML models quality assurance strategy
- AI/ML models continuous delivery/deployment strategies
- Communication with customers/partners/media/internal
- Hiring/Training/Mentoring

**Summary of Requirements**:
**Responsibilities**:

- Identify AI projects of high business impact including both product-related projects, and also research projects. Collaborate/communicate effectively with product owners representing different product lines.
- Prepare an annual roadmap for implementation of different AI projects.
- Provide ideas and insights into new business models that could be enabled using AI. Become a go-to-guy for C-Suite executives for implementing AI in their functional areas.
- Layout the AI projects/products implementation processes (ML model development lifecycle) including project inception, exploration phase, model building phase, model deployment, and model retraining. Ensure the project implementation governance on the ongoing basis.
- Prepare the plan and oversee the implementation of the AI platform which will be used to deploy and host ML models.
- Play a key role in deciding technologies and related roadmap for development, testing, deployment of AI/machine learning models.
- Prepare the plan and oversee the implementation of quality assurance processes in relation to model testing by different stakeholders; Model performance testing, model acceptance testing by product managers/consultants/customers, other forms of testing as applicable (such as metamorphic testing, dual coding testing, blackbox, white-box testing etc)
- Prepare the plan and oversee the implementation of continuous delivery/deployment strategies (A/B testing, canary testing etc) of machine learning models.
- Prepare the strategy/plan for ethical AI and oversee its implementation across different AI projects. Get involved with the interaction related to ethical AI with stakeholders including customers/partners AI governance team, auditors, regulators on the ongoing basis.
- Prepare the plan and oversee the implementation of machine learning pipeline automation to be used for automated ML model retraining/testing across different AI projects.
- Prepare the plan and oversee the implementation of AI infrastructure to be used for model training/retraining and model deployment in production. Ensures the use of cloud services vis-a-vis local infrastructure for fulfilling different requirements.
- Hire a team of data scientists; Keep a check on newer hiring requirements on the ongoing basis. Provide training and mentoring to the team
- Take part in communication with customers, partners, media stakeholders

**Technical Proficiency**:

- Up-to-date with AI research areas and current trends
- Nuances related to building machine learning models including data preparation techniques, feature engineering techniques, building machine learning (ML) models and related ML algorithms
- Stay up-to-date with current developments in the field of AI - Safe AI, Ethical AI, Fair AI etc.
- Cloud AI/ML services on AWS, GCP, Azure
- Cloud computing services
- Knowledgeable about Big Data technologies and related cloud services
- Software development lifecycle, project implementation methodologies such as agile etc
- Software engineering principles
- DevOps concepts for ML pipeline automation
- Infrastructure knowledge for deciding physical/virtual m/c for model retraining etc.

**Professionalism**:

- Minimum 5 years’ experience in technical management or similar role
- Excellent communication skills
- Ability to work in a start-up environment
- Outstanding analytical and problem-solving skills