Back
Specialized MLOps skillset with broad tooling requirements increases competition but not extremely generalist demand.
Role demands ML-specific platform experience and tooling, making cross-industry transfers difficult without MLOps background.
Extensive mandatory tech stack and platform experience requirements create strict screening filters for candidates.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, build, and operate AWS cloud-native ML & GenAI infrastructure, including deploying solutions to Kubernetes clusters across cloud and on-prem environments.
Develop and maintain multi-stage ETL pipelines supporting ML model training and real-time inference, ensuring platform reliability and observability.
Collaborate with ML engineers and platform stakeholders to enforce platform engineering best practices and enhance ML development and deployment workflows.
Proven experience with AWS managed services like SageMaker, Bedrock, EKS, EC2, S3, Lambda, API Gateway, RDS, CloudTrail, and CloudWatch.
Proven experience with Kubernetes on cloud and on-premises environments.
Proven experience designing and operating multi-stage ETL pipelines for ML training and inference.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in both ML lifecycle infrastructure and platform engineering with a reliability mindset focusing on scalable, observable systems.
Able to collaborate cross-functionally with ML engineers and backend engineers to align infrastructure with model development and deployment needs.
Pragmatic and standards-driven, favoring reusable platform patterns over ad-hoc solutions, with hands-on expertise in Infrastructure-as-Code tooling.