Azure Technical Architect and ML Engineer Manager

Azure Technical Architect and ML Engineer Manager

09 Jul 2024
District of Columbia, Washington, 56901 Washington USA

Azure Technical Architect and ML Engineer Manager

Vacancy expired!



Job Description

(Hybrid Remote, 80% Travel)
Role & Responsibilities



Leading large-scale development and operational deployment of Data and ML applications on Azure for business usage

Leading critical decision making for architecting and designing these applications around technologies, tools, algorithms, libraries, frameworks, deployment infrastructures on Azure Cloud

Design and package deployments of data engineering pipelines and machine learning models on Azure, for production environments working very closely with data scientists and data engineers.

Using experimentation code developed by Data Scientists as a basis, design and code production ready high-quality scalable application code for operational deployment.

Defining the pre-processing or feature engineering to be done on a given dataset and defining data augmentation pipelines on Azure

Define and incorporate software engineering practices into data engineering and data science model implementation code and deployments on Azure

Optimize data engineering pipeline performance and model performance and calibrate on appropriate combination of Azure infrastructure and Azure ML tools and services

Design and Implement APIs serving data requests as well as model outcomes, integrating with business applications, incorporate business rules and obtaining feedback

Design and implement optimal data and model stores and collection of data and model metadata, using Azure native services, during training and operations

Design and Implement data engineering pipeline monitoring and model monitoring and calibration solutions on Azure, for performance tracking of production deployed solution

Design and Implement containerized deployments of ML models and data products to Azure edge infrastructures

Design and Implement A/B testing approaches and standards for model evaluation

Candidates must be willing to travel up to 80%


Skills & Qualifications

Bachelor's degree in Computer Science, Engineering, Statistics, Technical Science or 2+ years of IT/Software Development/Programming experience
Minimum 3+ years' experience of building and deploying production applications for data products and embedded Deep Learning and Machine Learning models
Minimum 2+ years' experience in designing and deploying production data engineering pipelines and ML models (standalone and distributed) on Azure infrastructure and Azure native services .
Minimum 2+ years' experience setting and using model parameters and hyperparameters i.e. containerize and externalize to tune and scale the model for large datasets. Must have experience of deploying containerized models and ML pipelines using Docker, Kubernetes or equivalent technologies on Azure
Minimum 2+ years' experience in engineering models using frameworks such as TensorFlow, Kera's, SciKit, PySpark,OpenCV etc.
Minimum 3+ years' experience of using Jupyterhub, Anaconda , Spyder, Azure Databricks, Sagemaker,Flask for model engineering, deployments and monitoring.
Minimum 2+ years' experience using tools like Databricks MLFlow for designing and managing end-to-end machine learning lifecycle for tracking experiments, packaging ML code and deploying models from various ML libraries to model serving and inference platforms
Minimum 3+ years' experience of building, containerizing and deploying end to end automated data engineering and ML pipelines using technologies like Spark and Azure Native services in a large-scale production environment
Minimum 3+ years' experience with performance engineering of these models with very large-scale datasets on a large distributed infrastructure using technologies like Azure Synapse analytics, Azure Databricks, Azure API management .
Experience with Azure ML services as well as third party libraries that support learning models and algorithms.
Minimum 5+ years of strong programming skills in at least 2 languages from Python, Scala (and Spark), R, Java, C/C on Azure
Minimum 5+ years of deep understanding of software engineering and software architecture principles for building and deploying large-scale business critical applications.
Minimum 2+ years of understanding and experience in deploying containerized applications using Docker, Kubernetes or equivalent technologies on Azure

- Understanding of multiple Emerging Data and NoSQL Technologies
- Understanding of Data collection and analysis,
- Understanding of all phases of a complete Data Engineering and Dara Science Life-cycle
- Data and Analytics Pipeline Design & Automation
- Knowledge of multiple machine learning and deep learning techniques, technologies, tools
- Understanding of Statistical modeling,
- Strong Experience delivering and deploying medium to large scale solutions
- Knowledge and Understanding of Azure DevOps and MLOps
- Design Thinking
- Prior experience of consulting
- Background in data structures, and object-oriented programming


Job Details

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