What You'll Do Design, train, and deploy machine learning models for real-world applications (NLP, computer vision, forecasting, or recommendation systems). Build and optimize scalable ML pipelines using tools like Airflow, Kubeflow, MLflow, or SageMaker. Collaborate with data engineers to ensure clean, reliable data flows from source to model. Implement monitoring, drift detection, and retraining strategies for production models. Prototype new AI capabilities and translate research into production-ready features. Mentor junior engineers and contribute to technical strategy and architecture decisions. Champion best practices in MLOps, model governance, and ethical AI. What You Bring Required: Candidates must be reside in Philippines and other South East Asia. 3+ years of hands-on experience building and deploying ML models in production. Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn, Hugging Face). Experience with cloud platforms (AWS, GCP, or Azure) and infrastructure-as-code (Terraform, CloudFormation). Solid understanding of data structures, algorithms, and software engineering best practices. Familiarity with containerization (Docker, Kubernetes) and CI/CD for ML workflows. Excellent communication skills and ability to explain technical concepts to non-technical stakeholders. Preferred (Nice-to-Have): Experience with LLMs, RAG pipelines, or generative AI applications. Background in data engineering: Spark, Kafka, dbt, Snowflake, or BigQuery. Contributions to open-source ML projects or published research. Startup experience or interest in building products from 0→1. Knowledge of model monitoring tools (Evidently, WhyLabs, Arize) or feature stores (Feast, Tecton).