Role: MLOPS Architect (Machine Learning / AI Architect)
As an MLOPS Architect, you will play a crucial role in designing and implementing robust MLOps solutions, ensuring seamless scalability, flexibility, and efficient resource utilization for machine learning and AI initiatives. This position involves a blend of cloud architecture design, data pipeline development, MLOps implementation, and infrastructure as code practices, all while adhering to security and performance optimization standards.
Responsibilities
- Designing Cloud Architecture: Responsible for designing cloud architectures, preferably on AWS, Azure, or multi-cloud environments, that 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, managing cloud resources programmatically to ensure 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, considering factors like auto-scaling, load balancing, and efficient use of compute resources.
- Monitoring and Troubleshooting: Set up monitoring and alerting for the MLOps infrastructure and 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.
Requirements
- 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 to 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 is 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.