Uni Internship Jan to July 2027 - Developing and Evaluating Agentic AI Workflows

Join Synapxe as an intern and see how you can contribute in powering a healthier Singapore. Internship@Synapxe is where curiosity meets impact! You would be able to gain practical experience, hone your skills, and be part of meaningful work that improves health through technology!

As an intern you will join the Data Science & AI team to explore emerging agentic AI technologies and their application in healthcare. Recent advancements in agentic AI frameworks, tool-augmented Large Language Models (LLMs), multimodal models, and local/private model deployment present new opportunities to build more capable, context-aware, and privacy-preserving AI workflows.This internship focuses on developing, evaluating, and prototyping agentic AI workflows for healthcare use cases. Interns will explore agent frameworks, assess open-source models for local/private deployment, conduct benchmarking and evaluation activities, and support proof-of-concept implementations aligned with organisational priorities.

The selected intern(s) will assist in following:

• Conduct literature reviews and industry scans on foundation models, including LLMs, multimodal models, computer vision models, multi-agent systems, and tool-augmented LLM frameworks
• Explore agentic AI frameworks, including Claw variants and relevant alternatives, and assess how they can be adapted for healthcare-related workflows
• Assist in designing and running controlled experiments to compare models, agent frameworks, deployment configurations, and tool-use workflows
• Develop or support proof-of-concept implementations to demonstrate selected agentic AI capabilities
• Perform benchmarking of open-source models for local/private deployment
• Evaluate workflow-level performance
• Document system designs, technical setup, experiment results, limitations, and key findings
• Prepare presentation materials and support knowledge sharing within the team

About You:

• Undergraduate currently in Year 2 or Year 3, pursuing a degree in Business Analytics, Business Artificial Intelligence Systems, Information Systems, Computer Science, Computer Engineering, Data Science, or a related discipline
• Strong proficiency in Python programming, with solid coding fundamentals
• Strong interest in Generative AI, Large Language Models (LLMs), Agentic AI, and emerging AI paradigms
• Familiarity with LLM usage, including API-based models or open-source models
• Understanding of advanced AI concepts, such as Retrieval-Augmented Generation (RAG), tool-use workflows, and multi-step reasoning systems
• Experience or familiarity with agentic AI frameworks is preferred
• Familiarity with GitHub and collaborative development workflows is preferred
• Exposure to local or private deployment of LLMs (e.g. on-premise or GPU environments) is highly preferred
• Familiarity with model serving frameworks (e.g. Ollama, vLLM) and containerisation tools (e.g. Docker) is a strong advantage
• Exposure to cloud platforms (e.g. Azure, AWS, GCP) is a plus
• Independent, fast-learner, and self-driven 
• Good team player with strong analytical and communication skills 
• Ability to multitask and work effectively as part of a multidisciplinary team
• Passionate and keen to make a difference to re-imagine the future of HealthTech

Note: The scope of the project may change depending on organisational priorities, technical feasibility, and project progress. In addition, the student may be asked to support other ongoing AI-related projects and ad hoc duties where relevant.

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