MLOPS Architect Machine Learning AI Architect

Position: MLOPS Architect (Machine Learning / AI Architect)

Location: Remote

Skill Matrix

Required Skills

Rating

(Scale 110)

Work Experience (Years)

Last Used (Year)

Related Projects

/End Customer Names

AWS

Python

Airflow

Kedro or Luigi

AWS Python Airflow Kedro or Luigi

Hadoop Spark or similar frameworks. Experience with graph databases a plus.

1. Designing Cloud Architecture:

As an AWS Cloud Architect youll be responsible for designing cloud architectures preferably on AWS Azure or multicloud environments.

Your architecture design should enable seamless scalability flexibility and efficient resource utilization for MLOps implementations.

2. 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.

3.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.

4. 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.

5. 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.

6. Performance Optimization:

Optimize cloud resources for costeffectiveness and performance.

Consider factors like autoscaling load balancing and efficient use of compute resources.

7. 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.

8. Collaboration and Communication:

Work closely with crossfunctional 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.