AWS Architect (AI/ML)

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Contract Independent, Contract W2, 1 yr
Work from home available Travel required to 10%.

Job Description

Roles and Responsibilities

  • Design, develop, test, and deploy data pipelines, machine learning infrastructure and client-facing products and services. Scale existing ML models into production.
  • Know how to address technical problem solutions and implement them in practice with the help of native tools on AWS Cloud Platform
  • Ability to make decisions and take responsibility for projects and tasks
  • Analyze and resolve architectural problems, working closely with engineering, data science and operations teams
  • Perform technical architecture assessments and provide improvements and focus areas
    Provide best-practice knowledge, reference architectures, and patterns for use across ML engineering and architecture communities

Skills

  • Hands on experience with AWS Cloud focussed on services like Sagemaker, API Gateway, AWS Quicksight,
  • Practical experience in Machine Learning with experience developing and architecting software, conversant with a full lifecycle from prototype to production.
  • The technical know-how of AI/ML scenarios and operational challenges in production
  • Applying engineering principles to develop and deploy ML models in medium to large-scale environment
  • Proven experience with machine learning offerings in the AWS Cloud Platform, AWS Cloud certifications
  • Experience managing key elements of a data and ML platform: scalable data pipelines, feature stores, data lifecycle, model store, model deployment and monitoring, ML pipelines.
  • Experience of deploying models in a production environment (knowledge of modern pipeline frameworks like Kubeflow/TensorFlow Extended (TFX)
  • Strong experience in agile practices and CI/CD
  • Hands-on experience in the development, deployment and operation of data technologies and platforms such as:

  1. Datascience/Ml Libraries : Tensorflow, Keras, Numpy, Scipy, Pytorch, Scikit learn, PyMed termino, AWS Comprehend, AWS Textract
  2. IDE: Python, R, Jupyter, AWS Sagemaker Notebooks
  3. Integration - APIs, micro-services and ETL/ELT patterns
  4. DevOps - Ansible, Jenkins, ELK
  5. Version Control - Git, Bitbucket, native tools etc.
  6. Containerization - Docker, Kubernetes, AWS SageMaker.
  7. Languages and scripting: Python, R.
  8. Cloud Services AWS
  9. Visualization Tools - Looker, PowerBI, Tableau, AWS Quicksight, D3JS, MatPlotlib, Plotly, Seaborn.
Dice Id : 90995872
Position Id : 2021-36
Originally Posted : 2 years ago
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