AI/ML Engineer

1 day ago

Makati City, National Capital Region, Philippines Hitachi Digital Payment Solutions Philippines Inc. Full time ₱80,000 - ₱120,000 per year

About the Role

We are looking for a hands-on AI/ML Engineer who can take an idea from concept to deployment. You will be responsible for designing, developing, training, testing, and integrating machine learning models into core systems.

This role requires a versatile professional skilled in data science, software engineering, and MLOps to deliver end-to-end AI solutions that support business objectives.

What We Offer

  • Opportunity to develop and deploy AI solutions end-to-end.
  • Autonomy in technical design and implementation.
  • Direct impact on key business initiatives.
  • Collaborative environment with continuous learning opportunities.

Qualifications

  • Bachelor's or master's degree in computer science, Data Science, AI/ML, or a related field.
  • 3+ years of experience in applied machine learning or AI engineering.
  • Strong proficiency in Python and frameworks such as TensorFlow, PyTorch, or Scikit-learn.
  • Experience building and deploying APIs (e.g., FastAPI, Flask, Node).
  • Familiarity with cloud services (AWS, GCP, Azure) or containerization (Docker, Kubernetes).
  • Practical knowledge of data pipelines (ETL, data versioning, labeling tools).
  • Strong understanding of model evaluation and performance tuning

Bonus Skills

  • Experience with LLMs, prompt engineering, or LangChain.
  • Familiarity with vector databases (Pinecone, FAISS, Milvus).
  • Exposure to frontend frameworks (React, Vue) for prototyping.
  • Understanding of data security and compliance in regulated domains (e.g., fintech, healthcare).

Key Responsibilities

Data & Model Development

  • Collect, clean, and preprocess data for training and testing.
  • Design and develop machine learning models (classical ML or deep learning).
  • Train, tune, and validate models using real-world datasets.
  • Conduct performance testing and error analysis.

System Integration

  • Develop APIs or services to integrate the model into the main product or platform.
  • Collaborate with backend and frontend developers to embed AI functionality.
  • Ensure efficient inference and scalability in production environments.

MLOps & Deployment

  • Package and deploy models (e.g., via Docker, FastAPI, or cloud ML services).
  • Set up monitoring for model accuracy, drift, and system performance.
  • Maintain version control for models and data pipelines.

Collaboration & Documentation

  • Work closely with the Product Manager to translate business goals into technical solutions.
  • Document models, datasets, and architecture decisions.
  • Communicate findings and results clearly to non-technical stakeholders.

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