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Cloud Solutions Architect
2 months ago
Mandatory skills include proficiency in AWS, Python, and experience with workflow management tools such as Airflow, Kedro, or Luigi.
Preferred skills encompass familiarity with big data frameworks like Hadoop and Spark, along with experience in graph databases.
Cloud Architecture Design:
As a Cloud Solutions Architect, your primary responsibility will be to design robust cloud architectures, ideally leveraging AWS, Azure, or multi-cloud environments. Your designs should facilitate seamless scalability, adaptability, and optimal resource utilization for MLOps implementations.
Data Pipeline Development:
You will be tasked with creating data taxonomies and designing data pipelines that ensure effective data management, processing, and utilization across the AI/Client platform. These pipelines are crucial for the ingestion, transformation, and delivery of data to machine learning models.
MLOps Best Practices:
Collaborate with data scientists, engineers, and DevOps teams to implement best practices in MLOps. This includes establishing continuous integration and continuous deployment (CI/CD) pipelines for model training, deployment, and monitoring.
Infrastructure as Code (IaC):
Utilize tools such as AWS CloudFormation or Terraform to define and provision infrastructure resources. Managing cloud resources programmatically through Infrastructure as Code ensures consistency and reproducibility.
Security and Compliance:
It is imperative to ensure that the MLOps architecture complies with security best practices and regulatory requirements. Implement access controls, encryption, and monitoring to safeguard sensitive data and models.
Performance Enhancement:
Focus on optimizing cloud resources for both cost-effectiveness and performance. Key considerations include auto-scaling, load balancing, and efficient utilization of computing resources.
Monitoring and Issue Resolution:
Establish monitoring and alerting systems for the MLOps infrastructure. Be prepared to troubleshoot any issues related to infrastructure, data pipelines, and model deployments.
Collaboration and Communication:
Engage closely with cross-functional teams, including data scientists, software engineers, and business stakeholders. Effective communication is vital to align technical decisions with overarching business objectives.
Key Activities:
- Demonstrated experience in Python
- Background in developing data products, analytical models, and model governance
- Experience with AI workflow management tools such as Airflow, Kedro, or Luigi
- Exposure to statistical modeling, machine learning algorithms, and predictive analytics
- Strong organizational and planning skills
- Deep understanding of the AI development lifecycle and Agile methodologies
- Extensive experience with cloud computing platforms, particularly AWS
- Proven ability to deliver data products in environments with stringent security and model governance standards
