Description

You will own the development and deployment of end-to-end machine learning systems.

Responsibilities

• Design and build end-to-end ML pipelines, including model training, deployment, and monitoring.
• Develop scalable ML solutions using supervised, unsupervised, and deep learning techniques.
• Optimize models using TensorFlow, PyTorch, and SciKit-learn.
• Implement MLOps practices with Kubeflow for CI/CD and model management.
• Collaborate with data scientists and engineers to integrate ML systems into production.

Required Skills

• 6+ years of professional experience in machine learning engineering.
• Strong expertise in ML algorithms, deep learning frameworks, and statistical modelling.
• Hands-on experience with MLOps tools and cloud platforms including AWS, Azure, and GCP.
• Proficiency with TensorFlow, PyTorch, and SciKit-learn.
• Proven ability to create prototypes and document system designs.
• Excellent problem-solving and collaborative skills.

Key Skills

Education

Any Graduate