AI Engineer (Software Development Engineer)
Early-career AI engineer at AMD in Singapore building agentic AI systems and 'digital workers': developing LLM-powered agents, RAG pipelines, and prompt/context engineering, then deploying them into real business workflows using Python, agent frameworks (LangChain, LangGraph, etc.), vector databases, and cloud platforms.
- Compensation
- Not disclosed
- Location
- SG,Singapore
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Ai engineering
- Posted
Skills
Must have
- Degree in Computer Science, Artificial Intelligence, Software Engineering, Data Science, or a related technical field
Nice to have
- Strong Python programming skills
- Understanding of APIs, JSON, REST services, and software integration
- Familiarity with machine learning and natural language processing fundamentals
- Strong analytical and structured problem-solving abilities
- Ability to learn quickly in a fast-paced and evolving AI environment
- Strong communication and collaboration skills
- Experience with OpenAI, Azure OpenAI, Anthropic, Google Gemini, or similar LLM platforms
- Building LLM applications, chatbots, copilots, or AI assistants
- Retrieval-Augmented Generation (RAG) architectures
- Vector databases such as Pinecone, Weaviate, Chroma, Milvus, or Azure AI Search
- Agent frameworks including LangChain, LangGraph, CrewAI, LlamaIndex, AutoGen, or Semantic Kernel
- Function calling, embeddings, semantic search, and context management
- Prompt engineering and AI evaluation methodologies
- Cloud platforms such as Azure, AWS, or Google Cloud
- Docker, Kubernetes, CI/CD pipelines, or DevOps practices
- Git, software testing, and modern development workflows
- Curious and passionate about AI innovation
- Strong ownership mentality with a willingness to experiment
- Customer-focused mindset and ability to work directly with users
- Comfortable navigating ambiguity and solving complex problems
- Team player who thrives in a collaborative environment
Full description
ADVANCE YOUR CAREER. ADVANCE THE WORLD.
At AMD, we believe technology has the power to solve the world’s most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMD is shaping the future.
Whether you’re designing next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger — technology that moves the world forward. Join us and, together, we’ll advance your career.
THE ROLE:
We are seeking a passionate and highly motivated AI Engineer to join our AI Engineering team focused on building the next generation of Agentic AI and Digital Workers.
In this role, you will participate in the complete lifecycle of AI solution development, from prompt engineering and context engineering to deploying AI agents into real business environments. You will work alongside senior AI engineers, business stakeholders, and domain experts to build intelligent systems that automate workflows, enhance productivity, and transform enterprise operations.
This is an excellent opportunity for early-career engineers who are excited about Large Language Models (LLMs), AI agents, Retrieval-Augmented Generation (RAG), and enterprise AI deployment.
THE PERSON:
WHAT YOU WILL GAIN
- Hands-on experience building real-world Agentic AI systems
- Exposure to enterprise-scale AI deployment and Digital Worker platforms
- Mentorship from experienced AI engineers and architects
- Opportunities to work with cutting-edge LLM technologies and AI frameworks
- Direct involvement in AI transformation initiatives across multiple business functions
- Career growth path toward AI Engineer, Senior AI Engineer, AI Architect, or Forward Deployment Engineer roles
KEY RESPONSIBILITIES:Agentic AI Development
- Design, develop, and test AI agents capable of multi-step reasoning and workflow execution
- Build solutions leveraging LLMs, tool usage, memory, and agent orchestration frameworks
- Implement function calling, structured outputs, and automated workflows
- Develop Retrieval-Augmented Generation (RAG) pipelines using enterprise knowledge sources
- Optimize AI system performance, reliability, scalability, and response quality
- Assist in evaluating and benchmarking AI model effectiveness
Prompt & Context Engineering
- Design effective system prompts and task-specific prompts
- Build context retrieval and grounding strategies to improve AI accuracy
- Develop methods to reduce hallucinations and improve consistency