About the Role
Wilmar International is building enterprise AI capabilities that turn business problems into production-grade intelligent systems. As an Applied AI Engineer, you will implement AI solutions — particularly Generative AI and LLM-powered applications — that deliver measurable value across our operations, supply chain, finance, and corporate functions. You will build to the target AI architecture, reference patterns and standards set by the AI Platform Lead and Enterprise Architecture, taking direction on design and priorities while owning the hands-on implementation, testing and delivery of your assigned solutions. You will also apply AI-assisted development tools in your own day-to-day engineering work and support the wider adoption effort led by the AI Platform Lead. You will operate at the intersection of data, software engineering, and machine learning, taking solutions from concept to scaled production.
Key Responsibilities
- Build and implement applied AI solutions (LLM applications, RAG pipelines, intelligent automation, predictive models) from concept to production, following the AI architecture, reference patterns and standards set by the AI Platform Lead and Enterprise Architecture.
- Build, evaluate, and optimize prompts, retrieval pipelines, and agentic workflows using modern AI frameworks and APIs.
- Integrate AI capabilities into enterprise systems and applications as directed by architects and the AI Platform Lead, working alongside data engineers and business stakeholders, and flagging implementation-level trade-offs for the AI Platform Lead to resolve.
- Apply AI-assisted coding tools (e.g., GitHub Copilot, Claude Code, Cursor) in your own delivery work, trying out prompting techniques and workflows and feeding practical learnings back into the AI Platform Lead's adoption program.
- Apply responsible and effective use of AI across the software development lifecycle — from code generation and review to testing and documentation — following the security, IP, and code-governance guardrails set at the platform level.
- Contribute to enablement sessions, office hours, and knowledge-sharing run by the AI Platform Lead, sharing practical examples from your own delivery work to help upskill engineering teams.
- Build and run evaluation harnesses to measure model accuracy, latency, cost, and safety against the standards defined by the AI Platform Lead; iterate on solutions based on results.
- Operationalize models — deployment, monitoring, versioning, and lifecycle management (MLOps/LLMOps).
- Ensure solutions meet enterprise standards for security, data governance, responsible AI, and compliance.
- Support junior engineers informally on delivery questions, and translate scoped requirements from the AI Platform Lead or Enterprise Architecture into working technical solutions.
Required Qualifications
- Bachelor's or Master's in Computer Science, Engineering, Data Science, or related field.
- 4–7 years of software engineering experience, with 2–3+ years building and shipping applied ML/AI solutions to production.
- Deep expertise building end-to-end LLM applications — including agentic workflows, Retrieval-Augmented Generation (RAG) systems, and advanced prompt engineering.
- Strong programming proficiency (Python preferred) with hands-on experience across GenAI frameworks (e.g., LangChain, LlamaIndex, or equivalent).
- Solid foundation integrating LLMs with external tools (APIs, databases) to build autonomous, multi-step systems.
- Full-stack awareness with proven experience productionizing ML/AI models — including API development, containerization, cloud platforms (Azure, AWS, or GCP), and CI/CD pipelines.
- Hands-on, daily experience with AI-assisted development tools (GitHub Copilot, Claude Code, Cursor, or similar), with genuine enthusiasm for sharing effective prompting techniques and workflows with peers.
- Solid understanding of data pipelines, vector databases, and software engineering best practices (testing, version control).
- Strong problem-solving skills and the ability to work independently in an ambiguous, fast-moving environment.
- Fluency in both English and Mandarin (spoken and written).
Preferred Qualifications
- Experience with cloud AI services (Azure OpenAI, AWS Bedrock, Google Vertex AI).
- Familiarity with MLOps/LLMOps tooling and observability for AI systems.
- Exposure to enterprise environments — data governance, security, and responsible AI practices.
- Experience in agribusiness, manufacturing, supply chain, or FMCG is a plus.
- Some experience mentoring junior engineers or running informal knowledge-sharing sessions.
- Exposure to additional programming languages such as Java, .NET, or TypeScript.
