Description

You will build and support scalable, highly available machine learning and deep learning platforms across cloud and on-premises environments.

Responsibilities

• Build, configure, and scale ML platforms in Azure and on-premises to power data and analytics products.
• Design and maintain ML/DL pipelines and workflow orchestration, covering data collection, transformation, training, validation, serving, and monitoring.
• Implement ML/DL solutions that prioritize performance, scalability, model governance, and traceability.
• Collaborate with data scientists, architects, and DevOps engineers to implement scalable solutions for complex problems.
• Research and iterate on new technologies, frameworks, and tools related to ML/DL workflows.

Required Skills

• 4+ years of experience delivering DevOps and MLOps in a production or enterprise setting.
• Proficiency in Python or R with a strong understanding of the statistical foundations of ML algorithms.
• Hands-on experience with ML frameworks such as Scikit-learn, Keras, Theano, TensorFlow, or Spark MLlib.
• Experience with container-based deployment using Docker and Kubernetes.
• Experience managing and deploying large distributed systems like Spark, Dask, or H2O.
• System administration experience with Unix or Linux systems.
• Deep understanding of the machine learning modeling lifecycle and technical model delivery.
• Bachelor’s degree required.

Preferred Skills

• Experience with HPC tools including Nvidia and CUDA.
• Hands-on experience with IBM Watson Machine Learning systems.
• Experience with configuration management tools like Ansible or Puppet and monitoring platforms like Grafana or Zabbix.

Key Skills

Education

ANY GRADUATE