Machine Learning Operations Engineer

We are currently seeking an experienced DevOps Engineer who is passionate about deploying, automating, maintaining, and managing production systems. The ideal candidate will ensure that our systems are robust, secure, and able to give the best performance.

Key Responsibilities Manage and optimize DL infrastructure (Azure, Blob Storage, VNETs, hybrid/on-prem clusters). Scale and maintain Kubernetes clusters for distributed training and inference. Benchmark next-gen hardware (H100 vs. H200, cloud vs. deployment nodes) for cost/performance efficiency. Build high-performance multimodal data pipelines (Ray, PyArrow, SQL). Design robust CI/CD pipelines (GitHub Actions) and monitoring dashboards (Grafana, Prometheus). Support embedded evaluation (QNN boards, edge devices) and model compression validation. Drive strong software architecture, code quality, and dependency management. Requirements 2+ years in MLOps, Data Engineering, or DL Infrastructure. Expert Python skills with strong system design knowledge. Hands-on experience with Kubernetes, Docker, and Azure (ML, Storage, Networking). Experience optimizing GPU data pipelines. Strong ownership mindset and collaborative approach. Nice to Have: Experience with PyTorch workflows, embedded AI evaluation, and hardware-in-the-loop setups.