AI Engineer Jobs in Singapore | Generative AI & LLM Jobs
Job Overview
Explore AI Engineer Jobs in Singapore for technology professionals interested in building enterprise-grade Artificial Intelligence, Machine Learning, Generative AI, and Large Language Model solutions.
AI Engineers are responsible for transforming business requirements and complex datasets into scalable AI-powered applications. The role covers the complete AI engineering lifecycle—from data preparation and model development to LLM integration, Retrieval-Augmented Generation (RAG), AI agents, cloud deployment, MLOps, model monitoring, and production optimization.
Job Title
Position: AI Engineer / Generative AI Engineer / Machine Learning Engineer
Location: Singapore
Experience Level: Entry Level to Senior / Lead Roles
Employment Type: Full-Time / Permanent / Contract Opportunities
Work Arrangement: On-site / Hybrid / Remote, depending on employer requirements
Job Function: Artificial Intelligence / Machine Learning / Generative AI / Software Engineering
Career Level: Entry, Associate, Mid-Level, Senior & Lead
Opportunities may be available across entry, mid-level, senior, lead, and AI architecture positions. Candidates with expertise in Python, Machine Learning, Generative AI, LLMs, LangChain, PyTorch, TensorFlow, Hugging Face, vector databases, AWS, Azure, Google Cloud, Docker, Kubernetes, and MLOps are highly relevant for modern AI engineering roles.
These positions are suitable for professionals looking to build intelligent applications, enterprise AI platforms, conversational systems, recommendation engines, predictive solutions, document intelligence platforms, and AI automation systems in Singapore’s technology ecosystem.
Roles & Responsibilities
- Design, develop, test, deploy, and maintain scalable Artificial Intelligence and Machine Learning solutions.
- Build production-ready AI applications using Python, Machine Learning, Deep Learning, NLP, Generative AI, and Large Language Models.
- Develop enterprise Generative AI applications using commercial and open-source foundation models.
- Design and implement Retrieval-Augmented Generation (RAG) architectures connecting LLMs with enterprise knowledge sources.
- Develop AI agents and agentic workflows capable of reasoning, tool usage, retrieval, API interaction, and workflow automation.
- Build intelligent assistants, enterprise search solutions, recommendation engines, document intelligence applications, and conversational AI systems.
- Integrate LLMs and AI models into applications through APIs, SDKs, microservices, and cloud-native architectures.
- Develop and optimize machine learning models for classification, prediction, recommendation, forecasting, anomaly detection, and automation use cases.
- Implement NLP solutions for semantic search, text classification, information extraction, summarization, question answering, and conversational interfaces.
- Work with Transformers, embeddings, vector search, reranking, prompt engineering, fine-tuning, and model evaluation.
- Build semantic retrieval solutions using vector databases such as Pinecone, Weaviate, Milvus, Chroma, FAISS, or pgvector.
- Develop APIs and AI microservices using FastAPI, Flask, or equivalent frameworks.
- Deploy AI/ML workloads using AWS, Microsoft Azure, or Google Cloud Platform.
- Build scalable AI environments using Docker, Kubernetes, serverless technologies, and cloud-native services.
- Implement MLOps and LLMOps pipelines for model versioning, deployment, testing, monitoring, evaluation, and retraining.
- Evaluate AI applications for accuracy, relevance, latency, hallucination, grounding, robustness, security, and operating cost.
- Optimize LLM inference, token usage, retrieval performance, response quality, and infrastructure utilization.
- Implement responsible AI principles covering privacy, explainability, security, governance, fairness, and regulatory requirements.
- Work with Data Scientists, Data Engineers, Software Engineers, Cloud Architects, DevOps teams, Product Managers, and business stakeholders.
- Conduct technical experiments, proof-of-concepts, benchmarking, code reviews, architecture reviews, and production troubleshooting.
- Maintain technical documentation for AI architectures, APIs, models, data pipelines, deployment processes, and operational procedures.
- Senior professionals may define enterprise AI architecture, establish engineering standards, mentor team members, and lead strategic AI transformation initiatives.
Technical Skill Set
| Skill Area | Technologies / Competencies |
|---|---|
| Programming | Python, SQL, Java, Scala, JavaScript/TypeScript |
| Artificial Intelligence | AI Engineering, Machine Learning, Predictive Modeling, Generative AI |
| Generative AI | LLMs, Foundation Models, Prompt Engineering, AI Agents |
| LLM Ecosystem | OpenAI-compatible APIs, Claude, Gemini, Llama, Mistral, Open-Source LLMs |
| AI Frameworks | LangChain, LlamaIndex, Hugging Face, Semantic Kernel |
| Machine Learning | Scikit-learn, XGBoost, LightGBM, Feature Engineering |
| Deep Learning | PyTorch, TensorFlow, Keras, Neural Networks |
| NLP | Transformers, BERT, Embeddings, NER, Semantic Search |
| RAG | Retrieval-Augmented Generation, Chunking, Embeddings, Hybrid Search, Reranking |
| Vector Databases | Pinecone, Weaviate, Milvus, Chroma, FAISS, pgvector |
| AI Agents | Agentic AI, Tool Calling, Function Calling, Workflow Automation |
| Cloud AI | AWS AI/ML, Amazon SageMaker, Azure AI, Azure Machine Learning, Vertex AI |
| MLOps / LLMOps | MLflow, Kubeflow, Model Registry, Model Monitoring, AI Evaluation |
| Containers | Docker, Kubernetes |
| API Development | FastAPI, Flask, REST APIs, Microservices |
| Data Engineering | Apache Spark, Kafka, ETL/ELT, Data Pipelines |
| Databases | PostgreSQL, MySQL, MongoDB, Redis, NoSQL |
| DevOps | Git, CI/CD, GitHub Actions, Jenkins, Terraform |
| AI Security | AI Guardrails, Access Control, Data Privacy, Model Security |
| Responsible AI | AI Governance, Explainable AI, Bias Evaluation, Model Risk Management |
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, Mathematics, or a related discipline.
- Strong programming proficiency in Python and familiarity with modern software engineering practices.
- Understanding of machine learning algorithms, statistics, probability, feature engineering, and model evaluation.
- Hands-on knowledge of at least one major ML or deep learning framework such as PyTorch, TensorFlow, or Scikit-learn.
- Understanding of Generative AI, LLMs, Transformers, embeddings, and modern AI application architectures.
- Experience working with structured or unstructured datasets.
- Familiarity with databases, SQL, APIs, Git, and software development workflows.
- Ability to translate business requirements into practical AI/ML solutions.
- Understanding of model testing, validation, performance measurement, and production monitoring.
- Entry-level applicants should demonstrate relevant AI/ML projects, internships, academic research, certifications, or portfolio work.
- Experienced professionals should demonstrate successful development or deployment of production-grade AI/ML solutions.
- Senior candidates should possess strong system-design skills and experience making architecture-level decisions.
Preferred Qualifications
- Hands-on experience building production Generative AI and LLM applications.
- Strong understanding of RAG, vector databases, embeddings, semantic search, hybrid retrieval, and reranking.
- Experience with LangChain, LlamaIndex, Hugging Face, or similar AI frameworks.
- Experience designing AI agents and agentic workflows using tools, APIs, and enterprise systems.
- Knowledge of prompt engineering, structured outputs, function calling, LLM evaluation, and guardrails.
- Experience with model fine-tuning, parameter-efficient fine-tuning, inference optimization, or model compression.
- Hands-on experience with AWS, Microsoft Azure, or Google Cloud Platform.
- Experience with Amazon SageMaker, Azure AI, Azure Machine Learning, or Google Vertex AI.
- Knowledge of Docker, Kubernetes, Terraform, CI/CD, MLflow, or Kubeflow.
- Experience building scalable APIs and microservices using FastAPI or similar frameworks.
- Familiarity with multimodal AI involving text, image, audio, or document processing.
- Knowledge of responsible AI, AI governance, privacy, cybersecurity, and model-risk practices.
- Experience supporting high-volume or business-critical AI applications.
- Relevant cloud, AI, Machine Learning, or data engineering certifications are advantageous.
Desired Soft Skills
- Strong analytical and structured problem-solving abilities.
- Ability to convert business challenges into measurable AI solutions.
- Strong written and verbal communication skills.
- Ability to explain complex AI concepts to technical and non-technical stakeholders.
- Strong collaboration across engineering, data, product, security, and business teams.
- Curiosity and willingness to continuously learn emerging AI technologies.
- Ability to balance rapid experimentation with production-quality engineering.
- Strong ownership of model and application performance.
- Attention to data quality, AI accuracy, security, and reliability.
- Ability to troubleshoot complex AI and production issues systematically.
- Strong documentation and knowledge-sharing practices.
- Leadership, mentoring, stakeholder management, and architecture decision-making skills for senior positions.
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