AI Engineer

Experience: 3 to 10 years

Location: Quezon - Hybrid

Shift: Mid/Night shift

Job type: Permanent with MNC (Direct hire)

Responsibilities:

· Design, build, and deploy AI/ML solutions that integrate with enterprise data products, pipelines, and lakehouse architectures.

· Develop and operationalize machine learning models and AI services for use cases such as predictive analytics, anomaly detection, and automation.

· Design and implement Generative AI solutions using LLMs, including RAG architecture and prompt engineering.

· Collaborate with data engineers to embed AI capabilities into data pipelines and ensure seamless integration with data platforms (e.g., Fabric, Databricks).

· Partner with product owners, architects, and stakeholders to translate business needs into AI-driven solutions and reusable components.

· Enable AI readiness across DL&I data products by standardizing model integration, feature engineering, and inference patterns.

· Ensure AI solutions are production-ready by implementing monitoring, logging, and performance optimization practices.

· Support integration of AI outputs into data products, dashboards, and business processes, ensuring interpretability and usability.

· Participate in Agile delivery practices including backlog refinement, sprint planning, and continuous improvement.

Technical Skills:

AI & Machine Learning Engineering

· Machine learning model development and lifecycle management

· Feature engineering, model training, evaluation, and deployment

· Familiarity with supervised and unsupervised learning techniques

· Experience with model serving and inference pipelines

Cloud AI & Data Platforms

· Azure AI services (Azure Machine Learning, Cognitive Services, OpenAI integration)

· Microsoft Fabric AI capabilities (Copilot, AutoML, intelligent insights)

· Databricks (MLflow, Model Registry, Delta Lake)

· Understanding of Lakehouse architecture and AI integration patterns

Data Engineering & Integration

· Strong Python and/or SQL for data processing and model integration

· Experience with data pipelines and orchestration tools

· Knowledge of data transformation and feature pipelines

· Integration of AI outputs into downstream analytics systems

MLOps & Deployment

· CI/CD pipelines for machine learning models

· Model versioning, monitoring, and retraining strategies

· Logging, observability, and performance tuning of AI solutions