## **What You'll Do** * Design, train, and deploy production-grade machine learning models across NLP, computer vision, forecasting, or recommendation systems. * Build and optimize scalable ML pipelines using tools like Airflow, Kubeflow, MLflow, or SageMaker. * Partner with data engineers to ensure clean, reliable, and well-structured data flows from source to model. * Implement monitoring, drift detection, and automated retraining strategies to maintain model performance in production. * Prototype innovative AI capabilities and translate research concepts into production-ready features. * Mentor junior engineers and contribute to technical strategy, architecture, and best practices. * Advocate for MLOps excellence, model governance, and ethical AI practices across the organization. ## **What You Bring** **Required:** * Based in the Philippines or Southeast Asia. * 3+ years of hands-on experience building and deploying ML models in production. * Strong Python skills and proficiency with ML frameworks such as PyTorch, TensorFlow, scikit-learn, and Hugging Face. * Experience with cloud platforms (AWS, GCP, or Azure) and infrastructure-as-code tools (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, capable of explaining complex technical concepts to non-technical stakeholders. **Preferred (Nice-to-Have):** * Experience with LLMs, RAG pipelines, or generative AI applications. * Background in data engineering tools like 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).