The AI Engineer will be a key contributor to HEINEKEN's Global GenAI Lab in Singapore, responsible for designing, developing, and deploying end-to-end AI solutions that deliver measurable business value across the organization. This role combines deep expertise in Generative AI, Machine Learning, and Software Engineering to build scalable, secure, and production-ready AI applications.
As part of a fast-moving and innovative team, you will operate with the agility of a start-up while leveraging the scale and reach of a global enterprise. You will work with cutting-edge AI technologies to solve complex business challenges and accelerate HEINEKEN's digital transformation journey through impactful GenAI solutions.
The ideal candidate is a highly adaptable engineer with a strong technical foundation, curiosity to learn new domains, and the ability to translate emerging AI capabilities into practical business outcomes. You will be expected to take ownership of solutions end-to-end, collaborate closely with business stakeholders, and contribute to the evolution of the lab's engineering, MLOps, and DevOps capabilities.
Key Responsibilities:
AI Solution Development
- Design, build and operate production-grade GenAI systems in Python: agents and tool use (MCP, function calling), RAG backends, document parsing pipelines, APIs and containerised services on Azure.
- Define and implement evaluation and observability for everything the lab ships; make quality measurable and reportable to stakeholders.
- Build end-to-end AI solutions that integrate seamlessly with existing enterprise systems and workflows.
- Create functional demonstration interfaces and prototypes.
GenAI Devops and ML Ops
- Manage and own cloud infrastructure
- Advise the team on best practices for implementing cloud architecture for AI solutions
- Collaborate with the organization to set standards on AI enabled engineering
Software Engineering & API Development
- Drive engineering standards (code review, SDK packaging, documentation on Confluence/DevOps).
- Build robust, scalable APIs and microservices that serve AI models in production environments.
- Develop containerized applications using Docker and orchestration platforms for reliable deployment.
- Create and maintain clean, well-documented code that follows best practices for enterprise software development.
- Implement proper error handling, logging, and monitoring for AI applications.
Data Pipeline Engineering
- Design and implement robust data pipelines for preparation, cleaning, and integration of diverse data sources.
- Handle enterprise data challenges including Excel files, PowerPoint presentations, and Office 365 integrations.
- Build ETL processes that ensure data quality and consistency for AI model training and inference.
- Implement data processing solutions that scale efficiently with growing data volumes.
- Develop data validation and monitoring systems to maintain pipeline reliability.
Enterprise Integration & Deployment
- Lead technical scoping with product owners; convert ambiguous business asks into defined user stories, inputs and expected outputs; hold scope on POCs.
- Integrate AI solutions with existing business systems, databases, and enterprise applications.
- Navigate complex enterprise environments and work with legacy systems and data formats.
- Implement security best practices and ensure compliance with enterprise governance requirements.
- Manage model lifecycle including version control, A/B testing, and performance monitoring.
Research & Innovation
- Track and evaluate emerging models, frameworks and tooling; run structured bake-offs and recommend what the lab adopts.
- Conduct applied research to solve novel business problems using state-of-the-art AI techniques.
- Evaluate and benchmark different AI models and approaches for specific use cases.
- Contribute to the lab's knowledge base and share learnings across the team
Key Requirements:
- Bachelor's or Master's degree in Computer Science, Data Science, AI/ML, or related technical field preferred.
- Strong consideration given to candidates with demonstrated expertise through portfolio work and contributions to AI projects.
- 5+ years of software engineering with strong Python and modern development practice; 2+ years hands-on with LLM applications in production.
- Shipped at least one agentic or RAG system that real users depend on.
- Built evaluation or observability for LLM systems.
Technical skills
- Cloud Architecture, DevOps and deployment (Azure, GCP)
- Agent frameworks, tool use and MCP; prompt and context engineering; structured outputs.
- RAG and vector search; document parsing and unstructured data; embedding and reranking models.
- FastAPI, Docker, CI/CD, Git; packaging internal SDKs.
- LLM evaluation, tracing and monitoring (Langfuse or similar); data pipelines with pandas or equivalent.
- Working knowledge of MLOps practices: versioning, A/B testing, cost and latency telemetry.