senior-ml-engineer

Automating ML deployment pipelines across dev, staging, and prod environments is handled through end-to-end production ML engineering tasks. The system provides automated monitoring, rollback capabilities, and orchestration for scalable inference systems.

Detecting model drift in production ML systems is achieved through built-in monitoring and evaluation steps. The system automates drift detection alongside feature store integration to maintain real-time inference accuracy.

Docker and Kubernetes are fully supported for containerized ML model deployment. The system integrates with Python-based pipelines, PyTorch, and TensorFlow to orchestrate scalable real-time inference within these container environments.

LLM integration with RAG-enabled workflows for agentic AI applications is supported as a core feature. The system orchestrates these advanced models alongside traditional ML pipelines, feature stores, and governance tooling.

PyTorch and TensorFlow are both supported for scaling production ML inference systems, though the architecture relies primarily on Python-based pipelines. Docker and Kubernetes handle the underlying containerized scalability.