What are the responsibilities and job description for the MLOPS Engineer position at Shaarpro?
Job Description
Location: Remote, but candidate should be willing to work in CST timings.
Job Description:
Manage Google Cloud Platform platform data loads in and out of the platform or within hybrid environment
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
Design the data pipelines and engineering infrastructure to support internal clients- enterprise machine learning systems at scale
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
Communication and requirements from various stake holders to build final requirements and track progress
Qualifications
Experience building end-to-end systems as a Google Cloud Platform Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent)
MLOps within the enterprise CI/CD process for ML models
Experience deploying ML APIs in production environments in Google Cloud Platform using GKE
Experience in using Google Cloud Platform Vertex AI for ML and BigQuery
Knowledge in Terraform and Containers technologies
Experience writing data processing jobs using Google Cloud Platform Dataflow and Dataproc
Experience setting up ML model monitoring and autoscaling for ML prediction jobs
Strong software engineering skills in complex, multi-language systems
Fluency in Python and comfort with Linux administration
Experience working with cloud computing and database systems and cloud based various data formats NOSQL/HDFS
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
Experience in deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc.)