Role Summary
(Purpose: why does this role exist?)
We are seeking an experienced MLOps Engineer specializing in AWS to architect,
implement, and maintain robust machine learning operations infrastructure. This
role is crucial in bridging the gap between data science and production deployment,
ensuring seamless delivery of ML solutions at scale.
The ideal candidate will be responsible for building and maintaining scalable,
automated ML pipelines, implementing CI/CD practices, and optimizing cloud
infrastructure for ML workloads. They will work closely with data scientists and
software engineers to streamline the model deployment process, establish
monitoring systems, and ensure high availability of ML services.

Essential Responsibilities / Expectations
● Handling MLOps Infrastructure
● Design and implement end-to-end ML pipelines on AWS
● Set up and maintain CI/CD pipelines for ML models
● Automate model deployment, monitoring, and scaling
● Implement data versioning and model registry systems
● Establish MLOps best practices and standards
● Development & Implementation
● Build robust ML deployment architectures using AWS services
● Develop automated testing frameworks for ML systems
● Create monitoring and alerting systems for ML models
● Implement feature stores and model serving platforms
● Optimize infrastructure costs and performance
● Platform Management
● Manage AWS infrastructure using IaC (Terraform/CloudFormation)
● Configure and maintain container orchestration (EKS/ECS)
● Set up model experiment tracking systems
● Implement security best practices
● Handle capacity planning and resource optimization