MLOps Engineer (Edge and Accelerated Computing)

Key Skills:

• CI/CD for Machine Learning: Proficiency in automating complex ML pipelines, specifically integrating Hardware-in-the-Loop (HiL) testing stages.
• Container Orchestration at the Edge: Expertise in managing lightweight container runtimes and registry services for ARM64-based automotive platforms.
• Automated Benchmarking: Experience building "auto-profiling" scripts that capture low-level hardware metrics using system-level performance diagnostics.
• Model Lifecycle Tools: Deep knowledge of experiment tracking and model registry platforms used to manage high-dimensional VLM artifacts.
  Scripting & Integration: Strong Python and Shell skills for bridging disparate tools in the NVIDIA-based software stack.

Job Responsibilities

Key Responsibilities:

• Automated Deployment Pipelines: Build and maintain CI/CD workflows that automatically trigger model conversion, quantization, and deployment to the target hardware upon code or model updates.
• Environment Virtualization: Manage containerized development and execution environments to ensure consistency between high-power training servers and the resource-constrained target platform.
• Performance Observability: Implement automated monitoring for inference telemetry (latency, throughput, memory pressure, and thermal metrics) during automated test runs.
• Model Versioning & Lineage: Establish a system for tracking model iterations, ensuring that every optimized artifact is tied to its specific training data, quantization parameters, and hardware-specific plugins.
• Inference Service Management: Configure and optimize unified inference serving engines to handle concurrent requests and streaming data on the target SoC.

Department/Project Description

We are seeking a highly specialized MLOps Engineer (Edge & Accelerated Computing) to develop and manage the continuous deployment, optimization, and monitoring infrastructure for high-dimensional Vision-Language Models (VLM) running on resource-constrained, NVIDIA-based automotive platforms.

Skill Category

AI/ML