Business Analyst (AI Focus)
Summary
Business Analyst (AI Focus) who uncovers operational pain points, designs and supports AI prototypes, and runs workshops to drive AI literacy across a public-sector agency (MSF). Core focus on LLMs, prompt engineering, Python, and cloud-deployed ML/LLM solutions.
This role suits someone who thrives in a product-driven environment, enjoys connecting dots across teams, and communicates clearly and confidently in both technical and non-technical settings.
Job Responsibilities:
- Responsible for uncovering operational pain points and inefficiencies where AI could provide meaningful solutions and automation opportunities.
- Design and support development of AI prototypes to across divisions to maximise adoption and integration success.
- Monitor performance of deployed AI projects and develop strategies to expand successful implementations throughout MSF.
- Conduct workshops and collaborate with stakeholders to build AI literacy, raise awareness of approved AI tools, and enable public officers to adopt AI safely and responsibly in their day-to-day work.
- Track and report on AI initiative outcomes, including efficiency gains and cost savings, while supporting long-term AI strategy development.
- Develop reusable frameworks, standards, documentation, and training materials to support sustainable process excellence.
Job Requirement
- 2 to 4 years of experience as a technical business analyst, AI advocate, or innovation practitioner in a technology, preferably with hands-on involvement in AI awareness, adoption, or innovation initiatives.
- Good understanding of LLMs, how they work, how to do prompt engineering effectively, and how to build apps with them, their applications, and associated risks.
- Keen interests in AI development, including development in global AI regulations and guidelines.
- Preferably competent in Python programming, with some familiarity of cloud deployments for ML models and LLMs
- Strong written and verbal communication skills, with the ability to translate technical concepts for both technical and non-technical stakeholders.
- Structured, detail-oriented, and accountable, with strong documentation habits and follow-through.
- Comfortable working cross-functionally with engineering, product, policy, frontline teams, and external partners.
- Collaborative and humble, with a strong sense of ownership.