Machine Learning Engineer
Core
Design, build, and manage end-to-end machine learning pipelines and production-grade ML infrastructure, bridging Data Science, Engineering, and DevOps teams.
Role type
MLOps Engineer
Builds
Scalable ML deployment, monitoring, automation, and lifecycle management systems
Domain
Enterprise-scale AI/ML systems
Deliverable
production ML models
Required skills
Python, TensorFlow, PyTorch, Scikit-learn, Docker, Kubernetes, CI/CD tools (GitHub Actions, Jenkins, Argo CD), Cloud platforms (AWS, Azure, GCP)
Preferred skills
ML governance, security, compliance, reproducibility, version control, feature stores, model registries
Technologies
TensorFlow, PyTorch, Scikit-learn, Docker, Kubernetes, GitHub Actions, Jenkins, Argo CD, AWS, Microsoft Azure, Google Cloud Platform
Responsibilities
Design and maintain CI/CD pipelines for ML models and data; Automate model training, validation, testing, deployment, and rollback; Deploy ML models as REST APIs/microservices; Implement model monitoring and data drift detection; Manage and optimize cloud infrastructure; Maintain feature stores, model registries, and ML lifecycle tools; Troubleshoot and debug production ML systems
Seniority
Mid-level to Senior, hands-on IC
