AI Engineer – MLOps
KATBOTZ®
full-time
Posted on:
Location Type: Remote
Location: United States
Visit company websiteExplore more
About the role
- Build and maintain ML pipelines for training, testing, and deployment
- Deploy machine learning and AI models into production environments
- Manage model lifecycle (training, deployment, monitoring, retraining)
- Automate workflows using CI/CD for ML models
- Monitor model performance, drift, and data quality
- Work with data scientists and AI developers to productionize models
- Manage model versioning, data versioning, and experiment tracking
- Deploy models on cloud platforms (AWS, Azure, GCP)
- Containerize applications using Docker and Kubernetes
- Implement monitoring and logging for ML systems
- Ensure scalability, security, and reliability of AI systems
Requirements
- 3–7 years in Machine Learning / AI / Data Engineering
- 2+ years in MLOps / Model Deployment / ML Pipelines
- Experience deploying models to production is mandatory
- Python
- Machine Learning
- MLOps tools and frameworks
- Docker
- Kubernetes
- CI/CD (GitHub Actions, Jenkins, GitLab CI)
- MLflow / Kubeflow / Airflow
- Data pipelines
- APIs (FastAPI / Flask)
- Cloud platforms (AWS / Azure / GCP)
- SQL / NoSQL databases
- Model monitoring and logging
- MLOps Tools (Important) Candidate should have experience in some of these: MLflow Kubeflow Airflow DVC Weights & Biases SageMaker Azure ML Vertex AI
Benefits
- Competitive compensation package
- Opportunities for professional development and career advancement.
- Flexible working conditions, with remote options available.
- Dynamic and supportive work environment.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
Machine LearningMLOpsPythonCI/CDData pipelinesAPIsSQLNoSQLModel monitoringModel deployment