MLOPS Architect (Machine Learning / AI Architect)

Mandatory Skills : AWS, Python, Airflow, Kedro, or Luigi

Seconday Skills : 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.