Machine Learning Engineer
Core
Design, develop, deploy, and optimize machine learning models and systems for cloud and edge environments to solve complex business problems.
Role type
Machine Learning Engineer (Production & Edge)
Builds
Production ML models, scalable ML pipelines, and edge-optimized inference systems
Domain
Cloud computing, Edge computing, Machine Learning
Deliverable
production ML models
Required skills
Python, Machine Learning algorithms, Cloud platforms (AWS), Containerization (Docker, Kubernetes), MLOps tools (MLflow, Kubeflow, Airflow), SQL, Software engineering principles, Model optimization (quantization, pruning, distillation), Edge computing frameworks (TensorFlow Lite, ONNX Runtime, OpenVINO, TensorRT)
Preferred skills
Large language models APIs, AI agent frameworks, Prompt engineering, RAG architectures, AI safety practices
Technologies
AWS, Docker, Kubernetes, MLflow, Kubeflow, Airflow, TensorFlow Lite, ONNX Runtime, OpenVINO, TensorRT, Git
Responsibilities
Design and implement ML models and algorithms; Optimize model performance and efficiency; Deploy ML models into production environments; Build and maintain scalable ML pipelines; Implement MLOps practices; Deploy and optimize ML models for edge devices; Work with large-scale datasets to prepare data for ML applications
Seniority
Mid-level, hands-on IC