Job Title: Azure Technical Architect/ML Engineer Manager
Job Type: Perm/Full-Time
Location: Remote + Travel
Estimated Start Date: Before End of Month
This role will require deeply technical, hands-on experience in all phases of the data engineering and machine learning life-cycle (designing, building, deploying, scaling, tuning, monitoring, etc.)
Candidates must have fluent understanding of data engineering pipelines and machine learning models for consideration.
Candidates must be open to frequent regional travel post Covid restrictions (up to 80%)
Overview: The Lead ML Engineer and Azure Technical Architect will work closely with data scientists and data engineers to design, build and deploy data engineering and machine learning solutions specifically on Azure. The Technical Architect/Lead ML Engineer is expected to have deep hands on experience in all phases of a data engineering and machine learning lifecycle (ie: design, harden, build, deploy, scale, tune, monitor data engineering pipelines and machine learning models)
- Leading large-scale development and operational deployment of Data and ML applications on Azure for business usage
- Optimize data engineering pipeline performance and model performance and calibrate on appropriate combination of Azure infrastructure and Azure ML tools and services
- 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.
- Define and incorporate software engineering practices into data engineering and data science model implementation code and deployments on Azure
- 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
- 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
- Minimum 5+ years of deep understanding of software engineering and software architecture principles for building and deploying large-scale business critical applications.
- Experience of building and deploying production applications for data products and embedded Deep Learning and Machine Learning models
- Experience of using Jupyterhub, Anaconda , Spyder, Azure Databricks, Sagemaker,Flask for model engineering, deployments and monitoring.
Minimum Technical Qualifications:
- Minimum 5+ years of strong programming skills in at least 2 languages from Python, Scala (and Spark), R, Java, C/C++ on Azure
- Experience in designing and deploying production data engineering pipelines and ML models (standalone and distributed) on Azure infrastructure and Azure native services .
- 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
- Experience in engineering models using frameworks such as TensorFlow, Kera's, SciKit, PySpark,OpenCV etc.
- 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
Benefits: Medical/Dental/Vision, 401K, PTO, Bonus Incentive, Incredibly Competitive Comp Structure
Please email firstname.lastname@example.org for consideration
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