MLOPS Engineer

Currently, we are looking for talented resources for one of our listed clients. If interested please reply to me with your updated resume or feel free to reach out to me for more details at

Title: MLOPS Engineer

Location: Remote

Duration: Long Term

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.)
  

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