ML Ops Engineer

Job description:

• 12+ Years Must.

What You’ll Do:

• Build and maintain scalable MLOps pipelines using AWS SageMaker
•  Support full ML lifecycle: ingestion → training → versioning → deployment
• Optimize models for real-time inference via APIs
• Detect and address data/model drift, automate re-training workflows
• Use feature stores and model registries effectively
• Collaborate across data science, ML, and engineering teams
• Architect end-to-end AI/ML solutions on Databricks

What You Bring:

• 5+ years in MLOps, ML Engineering, or Data Engineering
• Deep AWS SageMaker experience (Pipelines, Studio, Model Hosting)
• Hands-on with large-scale data (billions of records)
• Real-time model deployment experience with <100ms latency
• Familiarity with Databricks, CI/CD for ML, and monitoring
• Skilled in Python, Docker, Bash, Terraform/CloudFormation (nice to have)