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USER EXPERIENCE RESEARCHERS PTE. LTD. is seeking an AI Engineer to design, develop, and deploy AI-powered applications and ML solutions in production environments. You will build LLM-based features, integrate AI models into existing systems, and collaborate with software, data, and product teams to deliver scalable AI products.
The role emphasizes hands-on coding, model evaluation, and optimization of costs while aligning technology with business goals.
We are looking for an AI Engineer to design, develop, and deploy AI-powered applications and machine learning solutions. The role involves building LLM-based features, integrating AI models into production systems, and collaborating with software, data, and product teams to deliver scalable AI products.
Design, develop, and deploy AI/ML applications for production environments.
Build and integrate Large Language Model (LLM) solutions using APIs and open-source models.
Develop RAG (Retrieval-Augmented Generation) pipelines with vector databases.
Create and optimize prompts, AI agents, and workflow automation.
Fine-tune and evaluate machine learning and generative AI models.
Build scalable REST APIs and integrate AI services with existing applications.
Monitor model performance, improve accuracy, and optimize inference costs.
Collaborate with cross-functional teams to translate business requirements into AI solutions.
Bachelor's degree in Computer Science, AI, Data Science, or a related field.
3+ years of experience in AI, Machine Learning, or Generative AI development.
Strong proficiency in Python.
Experience with LLM frameworks (LangChain, LlamaIndex, or similar).
Hands-on experience with OpenAI, Azure OpenAI, Claude, or open-source LLMs.
Knowledge of RAG, embeddings, and vector databases (Pinecone, Milvus, FAISS, ChromaDB).
Experience with PyTorch or TensorFlow.
Familiarity with Docker, Git, CI/CD, and cloud platforms (AWS, Azure, or GCP).
Good understanding of API development and system integration.
Experience with AI agents and multi-agent architectures.
Knowledge of MLOps tools (MLflow, Weights & Biases, Kubeflow).
Experience deploying models on Kubernetes or cloud-native environments.
Understanding of NLP, computer vision, or speech AI is an advantage.