AI/ML Engineer-US
Budget: $25 – $50 USD
## **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).
* 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).
Related categories:
Python
Azure
CUDA
Machine Learning (ML)
Data Mining
Docker
Kubernetes
Terraform
Hugging Face
MLOps