Overview
Skills
Job Details
AWS, Python, Airflow, Kedro, or Luigi
Hadoop, Spark, or similar frameworks. Experience with graph databases a plus.
- Designing Cloud Architecture:
As an AWS Cloud Architect, you ll be responsible for designing cloud architectures, preferably on AWS, Azure, or multi-cloud environments.
Your architecture design should enable seamless scalability, flexibility, and efficient resource utilization for MLOps implementations.
- Data Pipeline Design:
Develop data taxonomy and data pipeline designs to ensure efficient data management, processing, and utilization across the AI/ML platform.
These pipelines are critical for ingesting, transforming, and serving data to machine learning models.
- MLOps Implementation:
Collaborate with data scientists, engineers, and DevOps teams to implement MLOps best practices.
This involves setting up continuous integration and continuous deployment (CI/CD) pipelines for model training, deployment, and monitoring.
- Infrastructure as Code (IaC):
Use tools like AWS CloudFormation or Terraform to define and provision infrastructure resources.
Infrastructure as Code allows you to manage your cloud resources programmatically, ensuring consistency and reproducibility.
- Security and Compliance:
Ensure that the MLOps architecture adheres to security best practices and compliance requirements.
Implement access controls, encryption, and monitoring to protect sensitive data and models.
- Performance Optimization:
Optimize cloud resources for cost-effectiveness and performance.
Consider factors like auto-scaling, load balancing, and efficient use of compute resources.
- Monitoring and Troubleshooting:
Set up monitoring and alerting for the MLOps infrastructure.
Be prepared to troubleshoot issues related to infrastructure, data pipelines, and model deployments.
- Collaboration and Communication:
Work closely with cross-functional teams, including data scientists, software engineers, and business stakeholders.
Effective communication is essential to align technical decisions with business goals.
Responsibilities:
- Strong experience in Python
- Experience in data product development, analytical models, and model governance
- Experience with AI workflow management tools such as Airflow, Kedro, or Luigi
- Exposure statistical modeling, machine learning algorithms, and predictive analytics.
- Highly structured and organized work planning skills
- Strong understanding of the AI development lifecycle and Agile practices
- Proficiency in big data technologies like Hadoop, Spark, or similar frameworks. Experience with graph databases a plus.
- Extensive Experience in working with cloud computing platforms - AWS
- Proven track record of delivering data products in environments with strict adherence to security and model governance standards.