• US Citizen

• Green Card

• EAD (OPT/CPT/GC/H4)

• H1B Work Permit

• Corp-Corp

• Consulting/Contract

• UG :- - Not Required

• PG :- - Not Required

• No of position :- ( 1 )

• Post :- 12th Apr 2022

Responsibilities
Design the data pipelines and engineering infrastructure to support our clients’ enterprise machine learning systems at scale
Take offline models data scientists build and turn them into a real machine learning production system
Develop and deploy scalable tools and services for our clients to handle machine learning training and inference
Identify and evaluate new technologies to improve performance, maintainability, and reliability of our clients’ machine learning systems
Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.
Support model development, with an emphasis on auditability, versioning, and data security
Facilitate the development and deployment of proof-of-concept machine learning systems
Communicate with clients to build requirements and track progress
Qualifications
Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent)
Strong software engineering skills in complex, multi-language systems
Fluency in Python
Comfort with Linux administration
Experience working with cloud computing and database systems
Experience building custom integrations between cloud-based systems using APIs
Experience developing and maintaining ML systems built with open source tools
Experience developing with containers and Kubernetes in cloud computing environments
Familiarity with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.)
Ability to translate business needs to technical requirements
Strong understanding of software testing, benchmarking, and continuous integration
Exposure to machine learning methodology and best practices
Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc.)