Computer Vision Engineer – Edge AI (Contractor)

Mid-to-Senior Level | NVIDIA Jetson Orin | Mining & Chemical Plant Operations

Function          Operations Technology Data Science – Artificial Intelligence & Data Science

About the Role

Mosaic’s team is seeking a Computer Vision Engineer to take vision models from development into reliable, real-time production on the edge. The consultant will optimize and deploy models (PyTorch to ONNX/TensorRT) on NVIDIA Jetson Orin embedded hardware among others, running in containerized ARM64 environments, to support process control, safety, and quality use cases across mining and chemical plant operations.

Representative use cases include real-time visual monitoring, equipment-state detection, anomaly identification, and other camera-based applications that support operational decision-making and integration with downstream systems. The role is hands-on and delivery-focused: performance, latency, and long-run stability on the device matter as much as model accuracy.

Focus area      

Edge model optimization & deployment        40%

Real-time video pipeline engineering 35%

Validation, profiling & production support     25%

Key Responsibilities

Edge Model Optimization & Deployment (~40%)

•          Convert and optimize PyTorch vision models (detection, segmentation, classification) to ONNX and TensorRT engines for NVIDIA Jetson Orin.

•          Package inference workloads as Docker containers on JetPack/L4T (ARM64) and manage reproducible builds and deployments to field devices.

•          Partner with data scientists to make models edge-ready (input sizing, architecture trade-offs, pre/post-processing).

Real-Time Video Pipeline Engineering (~35%)

•          Build and tune low-latency RTSP/GStreamer ingestion pipelines using hardware decode and zero-copy (NVMM) memory.

•          Develop DeepStream pipelines, including custom C++ parsers/plugins for non-standard model outputs where required.

•          Write performance-critical components in C++ and CUDA; use Python for integration, tooling, and prototyping.

•          Deliver model outputs to downstream systems (e.g., alerts, dashboards, PLC/control-system interfaces) in coordination with site automation teams.

Validation, Profiling & Production Support (~25%)

•          Profile end-to-end latency, throughput, GPU/CPU/memory utilization, and thermals (e.g., Nsight Systems, tegrastats) and remove bottlenecks.

•          Engineer for 24/7 stability: stream reconnection, watchdogs, backpressure and frame-drop control, logging, and health monitoring.

•          Support site commissioning, field testing, and troubleshooting, including onsite visits to mining and plant locations as needed.

•          Document deployment procedures, configurations, and runbooks so solutions can be reused and scaled across sites.

Required Qualifications

•          Mid-to-senior level experience in computer vision / ML engineering with proven delivery of models to embedded or edge vision systems in production.

•          Hands-on experience with NVIDIA Jetson Orin (AGX Orin, Orin NX, or Orin Nano).

•          Experience optimizing and deploying models with TensorRT and ONNX.

•          Proficiency in Python and PyTorch.

•          Docker containerization on ARM64 / JetPack / L4T.

•          Experience profiling and tuning for latency and long-running stability on constrained hardware.

•          Strong written and verbal communication; able to work directly with plant operations, automation, and IT/OT teams.

Preferred Qualifications

•          Strong NVIDIA DeepStream SDK experience, including custom plugins or output parsers.

•          Strong CUDA and C++ development skills.

•          RTSP/GStreamer video pipeline development with IP cameras.

•          Experience in industrial environments (mining, chemicals, manufacturing, energy) and with OT/control-system integration (PLC, OPC UA, Modbus).

•          Familiarity with ruggedized camera and edge hardware selection, network and power constraints at remote sites.

•          Experience with fleet management, remote updates, or monitoring for multiple edge devices.

•          Ability to work within enterprise change-control (MOC) and cybersecurity review processes.