Project Lead – AI for Science, Strategic Partnerships & Commercialization

AI Nova Pte Ltd is an enterprise specializing in artificial intelligence consulting, intelligent infrastructure, and managed data center services. As our business enters an exciting phase of growth and market expansion, we are scaling our capabilities to support our growing operations.

Position Overview

The Project Lead position provides dedicated capacity to develop and deliver projects alongside AI Nova’s infrastructure and engineering teams. The role will connect customer requirements with research expertise, assess technology and deployment options, and manage progress from initial evaluation to pilot delivery and commercial agreements. This requires coordinated ownership of technology selection, research partnerships, commercial rights, product development and delivery economics.

Key Responsibilities

The Project Lead works with researchers, engineers and infrastructure teams to define development plans, coordinate implementation and assess whether results meet the project’s scientific, operational and customer requirements.

1. Technology evaluation and application selectionFollow China’s research laboratories, AI developers and open-source communities, reviewing Chinese- and English-language publications and technical documentation. Evaluate foundation models, AI agents and open-weight releases for task performance, reproducibility, compute requirements, licensing and commercial suitability.Scientific applications. Work with domain specialists on molecular prediction, protein or enzyme candidate prioritisation, scientific imaging and research-data analysis. Define the scientific question and baseline before selecting a tool, and identify which claims require experimental validation.Generative applications. Assess text-to-video, image-to-video and multilingual content workflows where there is a clear customer need. Evaluate visual and temporal consistency, prompt adherence, controllability, generation time and cost perusable output, alongside local-language and cultural expectations.
2. Data preparation and model adaptationCoordinate data access, preparation and annotation with the research and engineering teams. Establish whether prompting or retrieval can meet the task before committing to fine-tuning. Where justified, support supervised fine-tuning, including parameter-efficient methods such as LoRA, and coordinate preference optimization or other post-training work as appropriate to the application and available evidence.Agree data provenance and usage rights, maintain reproducible evaluation records, and reserve independent test data excluded from training and development decisions. Review changes against the baseline so that additional complexity is supported by a measurable improvement.
3. Pilot development and validationTranslate user interviews, surveys and observation of existing workflows into product requirements, prototype plans and acceptance criteria. Bring researchers and prospective customers into pilot reviews, using structured feedback to refine the application. Coordinate laboratory validation through qualified partners where required; scientific conclusions remain the responsibility of the relevant domain leads.
4. Deployment and operational readinessCoordinate GPU resource planning, inference serving, data access and integration with customer systems. Before deployment, review output quality, reliability, response latency, operating cost and user satisfaction. Establish practical arrangements for data handling, security, monitoring, support and handover, and feed delivery experience into the next iteration.
5. Research partnerships and commercial deliveryLead the university collaboration workplan, coordinating research responsibilities, computing resources, training activities and milestones with institutional counterparts. Connect relevant China-based teams and Singapore scientific partners to defined work packages, adapting communication to academic, technical and commercial settings.
6. Track budgets covering compute, licenses, people, data and external validation. Translate pilot workload requirements into capacity and service plans with infrastructure colleagues. Report progress, direct contribution, cash collection and delivery risks to management, recommending changes or closure when warranted. Document technical decisions and partner knowledge, and help Singapore-based colleagues take increasing ownership of delivery.

Requirements& Qualifications

• A master’s degree or higher in AI, data science, statistics, computer science, computational science, quantitative economics or a related analytical discipline, supported by relevant applied experience is preferred.
• Demonstrate regional work experiences, worked with China-based technology companies, universities or research institutes, with clear personal responsibility for cross-border collaboration or delivery. University - industry experience in Indonesia or Southeast Asia is advantageous.
• Practical understanding of AI evaluation, deployment, fine-tuning and compute economics; with the ability to translate research and user feedback into product and business decisions.
• Familiarity with biotechnology AI and generative applications is an advantage.
• Professional English and Mandarin are necessary for technical-document review, substantive partner discussions and management reporting; Bahasa Indonesia is an advantage. Strong project ownership, multicultural teamwork and knowledge-transfer skills are required.