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• No of position :- ( 1 )

• Post :- 4th Dec 2023

·         Machine Learning Ops Engineer to build & support scalable, highly available, and robust Machine Learning (ML) /Deep Learning (DL) platforms using ML/DL frameworks, High-Performance Computing (HPC) machines, Data Science tools, products & services in the cloud and on-premises for client’s data & analytics organization.

·         The role will expose you to cutting-edge technologies related to ML/DL and the ideal candidate will be driven, focused, and enthusiastic about learning new technologies and implementing them.

Responsibilities:

·         Build, install, configure, manage, and scale state-of-the-art machine learning platform in the cloud (Azure preferred) & on-premises powering client's Data & Analytics products and solutions.

·         Work with data scientists, architects, DevOps engineers, and vendors to implement scalable ML/DL solutions in the cloud and on-premises to solve complex problems.

·         Creating & maintaining ML/DL pipelines and overall ML/DL workflow orchestration including but not limited to data collection, prep, transform, analyze, experiment, train, validate, serve, monitor, etc.

·         Implement ML/DL solutions addressing performance, scalability, and the governance/ traceability of machine learning models

·         Iterate quickly through the latest technologies, products, frameworks, and R&D on the latest information related to ML/DL frameworks, tools & services.

Qualifications:

·         4+ years’ experience delivering DevOps and MLOps in a Production/Enterprise setting

·         Bachelor’s degree required; Masters preferred in Computer Science or Data Science

·         Excellent written and oral communication and presentation skills.

·         Experienced in a technical role involving platform and infrastructure operation. 

·         System administration experience of Unix or Linux systems. 

·         Container-based deployment experience using Docker and Kubernetes. 

·         Proficient with the machine learning modeling lifecycle and comfortable addressing both functional and technical aspects of model delivery

·         Experience with managing, and deploying large distributed systems like Spark, DASK & H20, and heterogenous platform components.

·         Experienced with programming languages like Python or R and comfortable in understanding the statistical foundations of most used ML algorithms.

·         Experienced with Machine Learning frameworks:  Sci-kit, Keras, Theano, TensorFlow, SparkMlib, etc. 

·         Preferred hands-on experience IBM Watson Machine Learning systems or related preferred

·         Preferred hands-on experience with HPC – Nvidia, CUDA

·         Preferred experience with configuration Management tools like Ansible, puppet

·         Preferred experience in monitoring and performance analysis of Machine Learning platforms using tools like Grafana and Zabbix.