senior-ml-engineer

Automate MLOps pipelines by standardizing model deployment in containers, running staging tests, and executing canary rollouts. This ensures reproducible training, model registry updates, lineage tracking, and continuous drift monitoring to maintain production performance.

Detect data drift by integrating automated drift checks into your MLOps pipelines. Establish clear validation criteria and automated retraining triggers to evaluate model performance and promote updated models when production data distribution shifts.

Integrate retrieval-augmented generation (RAG) by applying production-grade patterns for LLM integration. Use cost-aware orchestration to manage retrieval processes, ensuring scalable and cost-effective AI system performance within your application architecture.

Yes, you can package models in containers and perform canary deployments to control risk. Automated retraining pipelines use triggers, evaluation metrics, and promotion criteria to validate models before they are promoted to production environments.

Optimize LLM and RAG costs by applying cost-aware orchestration patterns to your AI systems. Standardizing MLOps pipelines and deployment workflows controls infrastructure expenses while maintaining reliable model performance and data quality.

Feature stores ensure data quality and reproducibility in MLOps pipelines by providing a centralized repository for training and serving features. They automate lineage tracking and maintain consistency between training and production environments.