| Job Title/Role |
MLOPS Architect (Machine Learning / AI Architect) |
| Experience level required (Years) |
10+ |
| Mandatory required skills |
AWS, Python, Airflow, Kedro, or Luigi |
| Preferred/Desired skills |
Hadoop, Spark, or similar frameworks. Experience with graph databases a plus |
| Please share the Detailed Job Description |
- 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.
Activities
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. |
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