Machine Learning Engineer
Core
Design, build, and deploy scalable machine learning systems in production, focusing on robustness, monitoring, and maintainability of critical ML infrastructure.
Role type
Senior Machine Learning Engineer (MLOps & Infrastructure)
Builds
Production ML pipelines, model serving infrastructure, and automated retraining systems
Domain
Telecommunications / Cloud Infrastructure
Deliverable
production ML models
Required skills
Python, TensorFlow, PyTorch, Scikit-learn, XGBoost, MLflow, Airflow, TFX, Kubeflow, BentoML, Docker, Kubernetes, AWS, GCP, Azure, Spark, Kafka, SQL, NoSQL, Git, Terraform, Helm, Prometheus, Grafana, ELK stack
Preferred skills
Feature stores (Feast, Tecton), model quantization, edge/embedded ML, model governance, on-device ML, streaming ML, cross-functional leadership
Technologies
TensorFlow, PyTorch, Scikit-learn, XGBoost, MLflow, Airflow, TFX, Kubeflow, BentoML, Docker, Kubernetes, AWS, GCP, Azure, SageMaker, Vertex AI, Spark, Kafka, Terraform, Helm, Prometheus, Grafana, ELK stack
Responsibilities
Design and build robust ML pipelines for training, validation, and deployment; Collaborate with data scientists and DevOps to align components with project goals; Ensure seamless cloud integration with AWS and Azure; Build reusable infrastructure components adhering to DevOps and MLOps best practices; Monitor model performance and implement automated drift detection and retraining pipelines; Optimize models for performance, scalability, and cost efficiency
Seniority
Senior, hands-on IC