AI Engineer

NCS is a leading technology services firm that operates across the Asia Pacific region in over 20 cities, providing consulting, digital services, technology solutions, and more. We believe in harnessing the power of technology to achieve extraordinary things, creating lasting value and impact for our communities, partners, and people. Our diverse workforce of 15,000 has delivered large-scale, mission-critical, and multi-platform projects for governments and enterprises in Singapore and the APAC region.
 

As an AI Engineer who bridges traditional machine learning and modern generative / agentic AI. This role involves designing, building, and deploying end-to-end AI solutions including classical ML models, RAG-based applications, and lightweight agentic workflows. The successful candidate will contribute hands-on to client delivery projects, applying strong Python engineering and statistical rigor to develop scalable AI solutions that work reliably in production environments.

What will you do?

AI / ML Model Development & Deployment

• Design, build, and deploy end-to-end ML models including classification, regression, time-series, and NLP models.

• Integrate ML models into production systems and applications.

• Monitor model performance and conduct analysis to detect data drift, quality issues, and opportunities for improvement.

Generative AI & RAG Application Development

• Develop GenAI applications including RAG pipelines, prompt-engineered chatbots, and lightweight agentic workflows using frameworks such as LangChain, LlamaIndex, LangGraph, or Python.

• Implement effective RAG architectures including chunking strategies, embedding selection, vector store configuration (e.g., Qdrant, Milvus, pgvector), and retrieval evaluation.

Software Engineering & Development Practices

• Write clean, maintainable, and production-ready Python code.

• Participate in code reviews and contribute to shared libraries and internal AI platform components.

• Use Git, CI/CD practices, and containerisation tools such as Docker in development workflows.

Collaboration & Delivery

• Work closely with data engineers, MLOps engineers, and client stakeholders to move AI experiments into reliable production deployments.

• Document technical approaches, solution designs, and experimental results clearly.

Research & Innovation

• Stay up to date with the evolving AI landscape and introduce relevant tools, techniques, and best practices to the team.

Qualifications

The ideal candidate should possess:

• At least 3 years of hands-on experience in AI/ML engineering or data science roles
• Demonstrable track record of delivering at least 2–3 ML projects (beyond notebooks — production or near-production) and 1–2 GenAI / agentic AI projects
• Strong Python programming skills including Pandas, NumPy, Scikit-learn, and PyTorch or TensorFlow
• Solid understanding of machine learning fundamentals including feature engineering, model evaluation, bias-variance trade-off, and hyperparameter optimisation
• Hands-on experience building RAG chatbots and applications using LLM APIs such as OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, or equivalent
• Working knowledge of RAG architecture decisions including chunking strategies, embedding model selection, vector store configuration, and retrieval quality evaluation
• Practical experience with at least one vector database such as Qdrant, Milvus, pgvector, or equivalent
• Experience with at least one cloud platform such as AWS, Azure, or GCP for model training or deployment
• Proficiency with Git, CI/CD basics, and containerisation (Docker)
• Ability to read and write at least one additional programming language such as TypeScript/JavaScript, Java, Go, or advanced SQL