MLOPS ENGINEER (SENIOR)
Project: Enterprise-scale ML deployment on AWS/Kubernetes
Experience: 6 to 8 years in MLOps/DevOps with ML focus
Key Responsibilities:
- Build containerized ML pipelines (Docker, Kubernetes/EKS)
- Design and implement ML Training Pipelines
- Implement CI/CD for model deployment (GitHub Actions, ArgoCD)
- Deploy models to production with KServe/Seldon on Kubernetes
- Configure auto-scaling, monitoring (Prometheus, Grafana, CloudWatch)
- Maintain 99.5% system uptime and <5 min prediction latency
Must-Have Skills:
- Strong Kubernetes (EKS) and Docker expertise
- CI/CD pipelines (GitHub Actions, ArgoCD, GitOps)
- AWS services (EC2, S3, RDS, EKS, ECR, IAM)
- MLflow model serving and registry
- Infrastructure as Code (Terraform/CloudFormation)
- FastAPI, Gunicorn for model serving
Nice-to-Have:
- Apache Airflow orchestration
- Security hardening (VPC, IAM, encryption)
