AI Engineer

GenZero is an investment platform company focused on accelerating decarbonisation globally. Founded by Temasek, we seek to deliver positive climate impact alongside long-term sustainable financial returns by investing in opportunities with the potential to be nurtured into impactful and scalable solutions.

Driven by a common purpose to decarbonise for future generations, we recognise the need for a holistic and integrated approach to achieve a net zero world. At GenZero, we adopt a flexible investment approach across three focus areas to drive climate impact: (i) nature-based solutions that help protect and restore our natural ecosystems while benefiting local communities and biodiversity; (ii) technology-based solutions that deliver deep decarbonisation impact; and (iii) carbon ecosystem enablers which refer to companies and solutions that support the development of an effective, efficient, and credible carbon ecosystem.

For more information on GenZero, visit www.genzero.co.

We are looking for a graduate hire to design, build, and scale AI-enabled workflows across the firm. This person will work independently to translate investment and business workflows into repeatable AI systems, documented prompt libraries, and agent-based automations, partnering directly with business units to design and implement solutions.

Responsibilities

• Partner with business units to understand workflows and pain points, to identify automation opportunities and design tailored AI-enabled systems
• Build and maintain a firm-wide prompt and workflow library, including clearly documented prompts, reusable templates, and standard operating guides
• Develop single-agent and multi-agent workflows for the investment process, including research, financial analysis, and internal reporting, using LLM APIs and function calling to move beyond prompting into working systems
• Support the setup and implementation of necessary infrastructure, such as databases, cloud hosting, or access controls, as required
• Gather structured feedback from users on AI implementation to refine the AI-enabled workflow
• Lead internal enablement and training sessions so teams can adopt AI tools effectively and responsibly
• Track usage, quality, accuracy, time savings, and other adoption metrics to assess ROI of AI initiatives
• Establish best practices for prompt design, human review, escalation paths, and safe deployment of AI-enabled workflows across the organisation

Expected Deliverables

• A well-organised, firm-wide library of high-quality prompts, templates, and workflow documentation
• A portfolio of deployed single-agent and multi-agent workflows in active use across the organisation
• Clear documentation showing how each workflow works, when to use it, and where human review is required
• Regular reporting on usage, adoption, output quality, and workflow impact
• Practical recommendations on which AI tools and models the firm should use for specific use cases
• Training materials and examples that help teams adopt AI in day-to-day work

Requirements

• Completed degree in Computer Science, Data Science, Finance, or a related field
• Demonstrated experience turning prompts into usable workflows, tools, or automations, through internships, projects, or personal builds
• Ability to work independently with business units to scope and design workflow solutions with limited oversight
• Strong written communication skills, with the ability to create clear prompts, instructions, and user-facing documentation
• Strong analytical and problem-solving skills, with the ability to map business processes and identify where AI can add value
• Comfort working across both technical and non-technical teams

Preferred Experience

• Coding ability in Python, JavaScript, or similar languages
• Familiarity with private equity and investment workflows
• Experience creating internal playbooks, training materials, or operational documentation

Ideal Candidate Profile

The ideal candidate is hands-on and structured: able to independently scope a workflow with a business unit, build it using LLM APIs, document it, and iterate based on feedback. They are curious about how work gets done inside an organisation and comfortable owning a project from design through deployment with minimal hand-holding.

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