AI Engineer

We are looking for a passionate and skilled AI engineer who is responsible for designing and delivering complex generative AI solutions. This role requires the ability to work autonomously on challenging technical problems, provide mentorship to junior engineers and champion engineering best practices.

Due to the nature of the projects and specific client security clearance, this role requires the successful candidate to be a Singapore Citizen or PR of Singapore.

Job responsibilities

• Design, build and deploy GenAI applications using techniques such RAG, Agents, Multi Agent Systems etc., taking ideas from prototype to production.
• Work with both AI/ML engineers and software engineers to deliver reliable, scalable systems.
• Own key features or components, ensuring they are well-structured, efficient and easy to maintain.
• Make technical decisions and contribute to system design with attention to performance, cost and reliability.
• Collaborate with product managers, designers and data scientists to turn business needs into practical solutions.
• Share knowledge and mentor junior team members, helping them grow their technical skills.
• Review code and provide clear, constructive feedback to peers.
• Understand deployment options and trade offs across different cloud providers such as AWS, Azure, Google Cloud, etc.
• Understand optimization techniques to improve accuracy, performance and cost for GenAI applications.

Job qualifications

Technical Skills

• Strong software engineering fundamentals, including Python, CI/CD, testing, version control and clean code practices.
• Solid grasp of software design principles and ability to design and implement AI-powered components or workflows.
• Experience with at least one GenAI framework (e.g., LangChain, LlamaIndex, Semantic Kernel) and one agentic framework (e.g., PydanticAI, LangGraph, AutoGen2).
• Hands-on experience building and optimizing Retrieval-Augmented Generation (RAG) pipelines with vector databases (e.g., FAISS, Pinecone, Weaviate).
• Familiarity with deploying AI solutions on major cloud platforms (AWS, Azure or GCP), with basic use of containers (Docker) and CI/CD pipelines.
• Experience using LLMOps and observability tools (e.g., Langfuse, PromptLayer, OpenTelemetry) in production.
• Exposure to fine-tuning and use of ML/NLP frameworks such as PyTorch or Hugging Face Transformers.

Professional Skills

• Ability to independently solve complex, ambiguous technical problems.