Senior MLOps Engineer – AWS-Focused ML Infrastructure
Core
Design, implement, and maintain end-to-end MLOps pipelines on AWS to support the deployment, scaling, and operationalization of machine learning solutions for manufacturing and semiconductor analytics platforms.
Role type
Senior MLOps Engineer (AWS)
Builds
Production-ready ML workflows, CI/CD pipelines, and scalable infrastructure for Generative AI and classical ML models.
Domain
Manufacturing and Semiconductor Analytics
Deliverable
production ML models
Required skills
AWS SageMaker, CI/CD automation, Terraform/CloudFormation, model monitoring, drift detection, container orchestration (ECS/EKS), security compliance (IAM/VPC)
Preferred skills
Experience with Bedrock, RAG pipelines, vector databases (OpenSearch/Pinecone), Prometheus/Grafana, A/B testing
Technologies
AWS (SageMaker, CodePipeline, CodeBuild, Step Functions, Bedrock, OpenSearch, Pinecone, Lambda, EC2, S3, EMR, ECS, EKS, CloudWatch, X-Ray), Terraform, CloudFormation, XGBoost, Scikit-learn, Prometheus, Grafana
Responsibilities
Design and maintain end-to-end MLOps pipelines including CI/CD for model training, validation, deployment, and retraining; Operationalize AWS Bedrock workflows including RAG pipelines and agentic systems; Deploy and monitor classical ML models with drift detection; Manage infrastructure as code to provision and optimize AWS resources; Implement monitoring, logging, and alerting systems to track model performance and ensure high availability; Collaborate with ML engineers and SRE teams for A/B testing, versioning, and cost optimization; Enforce security and compliance best practices including IAM roles, VPC configurations, and data encryption.
