Senior AI Engineer

Company not disclosed

Singapore, Singapore
Salary not specified
Posted
Full-time
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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. 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.

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