Azure Data Engineer - Multiple Locations

Our client is looking for a Azure Data Engineer for Long-term Project in Multiple locations below is the detailed requirement.

Job Role: Azure Data Engineer with ML Experience

Location: Multiple Locations

Mode of Hiring: Long Term

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 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:

• 10+ years experience in Data Engineering space with full stack development, with hands-on experience in building machine learning production infrastructure (MLOps)
• Minimum 5+ year working experience streamlining the development, deployment, and management of machine learning models in production environments.
• Mandatory experience working in Databricks, Azure DevOps, and ML experience specifically in Databricks (ML flow, Feature Store, working w/ the model registry, etc.)
• Experience building end-to-end systems as a Platform Engineer/ ML DevOps Engineer
• Strong software engineering skills in complex, multi-language systems. Fluency in Python, Go or Bash, good understanding of Linux, knowledge of frameworks such as scikit-learn, Keras, PyTorch, Tensorflow, etc
• Experience working with cloud computing and database systems
• Experience building custom integrations between cloud-based systems using APIs.
• Experience on CICD pipelines orchestration experience - deploying machine learning solutions using DevOps principles is quite high
• Experience developing and maintaining ML systems built with open source tools
• Familiarity with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.)
• Exposure to machine learning methodology and best practices

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