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Machine Learning Engineer

Job in Singapore, Singapore
Listing for: FPT ASIA PACIFIC PTE. LTD.
Full Time position
Listed on 2025-11-23
Job specializations:
  • IT/Tech
    AI Engineer, Machine Learning/ ML Engineer, Data Scientist
Salary/Wage Range or Industry Benchmark: 125000 - 150000 SGD Yearly SGD 125000.00 150000.00 YEAR
Job Description & How to Apply Below

Machine Learning Engineer

  • We are seeking a Machine Learning Engineer to help prototype, evaluate, and operationalize emerging AI technologies for airport use cases.
  • You will work hands‑on with LLMs
    , RAG pipelines
    , and AI agents
    , helping the team rapidly test hypotheses, build proofs of concept (PoCs), and deploy pilot systems in real operational contexts.
Key Responsibilities
  • Prototype and evaluate foundation and multimodal models (e.g., GPT‑4/5, Claude, Gemini, Llama 3, Mistral).
  • Implement retrieval‑augmented generation (RAG) workflows using vector databases (FAISS, Pinecone, Weaviate).
  • Fine‑tune or adapt models using LoRA and PEFT techniques for airport‑specific data.
  • Explore agentic AI tools (e.g.,
    Lang Chain
    , Auto Gen
    , CrewAI
    , Lang Graph
    ) to automate operational tasks.
  • Build lightweight GenAI microservices with Python
    , FastAPI
    , and Docker for PoC deployment.
  • Work with cloud platforms (AWS, GCP) for rapid model hosting and experimentation.
  • Collaborate with data scientists and operations teams to test solutions in real‑world settings.
  • Document and share results, experiments, and learnings for future scalability.
Qualifications
  • Bachelors or Masters degree in Computer Science
    , Machine Learning
    , or related fields.
  • 2 - 5 years of experience in ML / GenAI development or applied research.
  • Proficiency in Python
    , Py Torch , and Transformers
    .
  • Hands‑on experience with LLM APIs
    , Lang Chain
    , or Llama Index
    .
  • Hands‑on experience with cloud ML platforms (AWS Sagemaker, Bedrock, GCP Vertex AI).
  • Comfortable with rapid prototyping
    , experimentation, and iterative design.
Nice-to-have
  • Exposure to multimodal or agentic AI systems.
  • Understanding of prompt design
    , evaluation metrics
    , or hallucination detection
    .
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