AI Summary
We are looking for a skilled AI/MLOps Engineer to own the deployment, monitoring, and continuous delivery of machine learning models in production environments. This is a fully remote role ideal for someone who is equally comfortable with ML pipelines and cloud infrastructure. The ideal candidate will have strong Python skills and experience with CI/CD tools, cloud platforms, and containerization.
Key Highlights
Deploy, manage, and monitor machine learning models in production environments
Build and maintain CI/CD pipelines for ML workflows
Collaborate with data scientists to package and productionize models
Key Responsibilities
Deploy, manage, and monitor machine learning models in production environments
Build and maintain CI/CD pipelines for ML workflows
Collaborate with data scientists to package and productionize models
Implement logging, alerting, and observability for ML systems
Ensure security, scalability, and cost-efficiency of ML infrastructure
Technical Skills Required
Benefits & Perks
Remote work
Full-time employment
Nice to Have
Experience with feature stores
Knowledge of data pipeline tools
Familiarity with LLM deployment and serving
Job Description
Location: Remote (Philippines)
Type: Full-Time
We are looking for a skilled AI/MLOps Engineer to own the deployment, monitoring, and continuous delivery of machine learning models in production environments. You will bridge the gap between data science and engineering — ensuring models are reliable, scalable, and performing as expected at all times. This is a fully remote role ideal for someone who is equally comfortable with ML pipelines and cloud infrastructure.
Responsibilities
- Deploy, manage, and monitor machine learning models in production environments
- Build and maintain CI/CD pipelines for ML workflows
- Automate model training, validation, versioning, and retraining pipelines
- Monitor model performance, detect drift, and trigger retraining when needed
- Manage cloud infrastructure for ML workloads on AWS, GCP, or Azure
- Collaborate with data scientists to package and productionize models
- Implement logging, alerting, and observability for ML systems
- Ensure security, scalability, and cost-efficiency of ML infrastructure
- Maintain documentation for all pipelines, deployments, and processes
- Evaluate and integrate MLOps tools and platforms (MLflow, Kubeflow,
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Requirements
- 2–3 years of hands-on MLOps or ML engineering experience
- Strong Python skills — scripting, automation, and ML pipeline development
- Experience with CI/CD tools (GitHub Actions, Jenkins, GitLab CI, or similar)
- Hands-on experience with at least one major cloud platform: AWS, GCP, or Azure
- Familiarity with containerization and orchestration: Docker and Kubernetes
- Experience with model monitoring, versioning, and experiment tracking (MLflow, DVC, or similar)
- Solid understanding of ML concepts — model training, evaluation, and deployment
- Strong problem-solving skills and ability to work independently in a remote setting
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- Experience with feature stores (Feast, Tecton, or similar)
- Knowledge of data pipeline tools (Apache Airflow, Prefect, or similar)
- Familiarity with LLM deployment and serving (vLLM, TGI, or similar)
- Experience with Terraform or infrastructure-as-code tools
- Exposure to real-time inference and model serving frameworks
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