Senior AI Engineer

The HEINEKEN Company; Singapore; On-site; SGD 180,000 - 240,000; Full time

Job summary

The HEINEKEN Company seeks an AI Engineer to join the Global GenAI Lab in Singapore. You will design, build and deploy end‑to‑end GenAI solutions across the organization, blending Generative AI, ML and software engineering to deliver measurable business impact.

You’ll contribute to building scalable, secure and production‑ready AI applications, while collaborating with business stakeholders and engineering teams to evolve lab capabilities in MLOps, DevOps and enterprise data handling.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML or related field.
• 5+ years of software engineering experience with strong Python and modern development practices.
• 2+ years hands‑on experience 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.

Responsibilities

• Design, build and operate production‑grade GenAI systems in Python: agents and tool use, RAG backends, pipelines, APIs and containerised services on Azure.
• Define and implement evaluation and observability for all lab outputs; quantify quality for stakeholders.
• Build end‑to‑end AI solutions that integrate with enterprise systems and workflows.
• Develop and maintain robust data pipelines, APIs, and containerised deployments.

Skills

Python

Software engineering

LLM applications

Observability

Agent frameworks

Education

Bachelor’s or Master’s in CS/AI/ML

Tools

Azure

GCP

FastAPI

Docker

CI/CD

Git

Job description

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.
• 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.
• 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.
• 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.