AI/ ML Engineer - Mid Level

Location: Makati City

Work Set Up: Hybrid

Project Based With Client

INTERVIEW PROCESS : Virtual interview - F2f interview - Final Interview

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.

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.

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.
• 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.