3D Point Cloud ML Architect/Developer at Remote

  • Posted 3 hours ago | Updated 3 hours ago

Overview

Remote
Hybrid
Full Time
Accepts corp to corp applications
Contract - Independent
Contract - W2
Contract - Month(s)

Skills

Video
Artificial Intelligence
Software Development
Analytics
Data Extraction
Modeling
Servers
Storage
Collaboration
Cloud Computing
3D Computer Graphics
Machine Learning (ML)
PyTorch
TensorFlow
Natural Language Processing
Optical Character Recognition
OpenCV
Docker
Git
Continuous Integration
Continuous Delivery
GPU
Continuous Integration and Development
Programming Languages
Python
C++

Job Details

Role: 3D Point Cloud ML Architect/Developer

Location: Remote

Duration: Long Term

Interview Mode: Video

Must have Skills:

3D Point Cloud ML Architect/Developer
Agentic AI framework
ML Engineering
3D point cloud data extraction

Description:

A Senior ML Engineer (>10 years of experience in software development of which last 3 years have done ML flow hands-on) for an on-premises solution. He/She will design and implement machine learning systems for advanced 3D point cloud analytics and document data extraction. This role prioritizes local infrastructure, high security, and data sovereignty, building solutions entirely within internal servers and networks.

Short Job Description
Develop, test, and deploy ML pipelines for 3D point cloud processing, including registration, filtering, segmentation, and modeling on local servers.
Extract and classify data from documents using OCR and NLP, all using solutions that are within on-prem environments.
Optimize performance for large data volumes leveraging local GPU clusters and accelerated computing.
Ensure robust integration with in-house IT systems and secure storage frameworks.
Collaborate with multidisciplinary teams for continuous solution improvement.

On-Prem Tools & Technologies
3D Point Cloud: Open3D, PCL (C++), CloudCompare, MeshLab.
ML/DL Frameworks: PyTorch, TensorFlow (on local machines or clusters).
NLP/OCR: spaCy, Tesseract, OpenCV, local implementations of HuggingFace models.
Infrastructure: Docker, Git, local CI/CD, on-prem GPU infrastructure.
Must have - Continuous development using Programming languages - Python, C++.

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