AI Engineer – Point Cloud to CAD Automation (Confidential Pilot Project)

AI Engineer – Point Cloud to CAD Automation (Confidential Pilot Project)Remote (U.S. preferred)Contract / Short-Term (3–6 months, potential extension)BigRio is a remote-based, technology consulting firm with headquarters in Boston, MA. We deliver software solutions ranging from: custom development, software implementation, data analytics, and machine learning/AI integrations. We are a one-stop shop that attracts clients from a variety of industries because of our proven ability to deliver cutting-edge and cost-conscious software solutions. OverviewWe are initiating a confidential proof-of-concept project exploring how AI and 3D computer vision can accelerate the transformation of point-cloud data into intelligent engineering deliverables. We're looking for an AI Engineer / Point Cloud Automation Specialist to design, prototype, and implement this pilot. The ideal candidate combines deep technical knowledge of 3D data processing with practical experience in automating engineering workflows. Key ResponsibilitiesLead the technical design and implementation of a workflow that transforms point-cloud data (e.g., E57, LAS, RCS) into usable CAD or isometric drawing outputs. Develop or fine-tune AI/ML models for: Segmentation and classification of 3D objects (e.g., piping, equipment, structure).Feature extraction and geometry reconstruction (centerlines, fittings, junctions).Build the supporting data pipeline: ingestion, filtering, model inference, and output generation. Evaluate and integrate available open-source or commercial libraries (Open3D, PCL, CloudCompare, PyTorch3D, etc.).Collaborate with a domain Subject Matter Expert (SME) to align the automation outputs with real-world engineering drawing conventions. Document technical architecture, model training processes, and pilot results. Present findings and propose next steps for scaling or production use. Required Skills & Experience5+ years of experience in 3D computer vision, point-cloud processing, or spatial AI.Strong Python programming experience and familiarity with modern 3D frameworks such as Open3D, PCL, PDAL, PyTorch3D, MinkowskiEngine, PointNet/PointNet++, etc. Demonstrated ability to extract and model geometry from unstructured 3D data. Experience integrating AI models with CAD or 3D modeling platforms (AutoCAD, Revit, Plant 3D, SolidWorks, or similar).Understanding of 2D/3D geometry projection and automated drawing generation workflows. Strong data-engineering fundamentals for building reliable, scalable processing pipelines. Preferred QualificationsPrior experience in as-built modeling, scan-to-CAD, or digital-twin applications. Background in mechanical, civil, or process engineering disciplines. Familiarity with industrial facility modeling standards or engineering documentation practices. Experience developing or deploying AI models for 3D segmentation and object recognition. Cloud or GPU training experience (AWS, Azure, or GCP).Comfortable operating in an R&D or prototype environment with limited pre-existing datasets. Soft SkillsSelf-starter with the ability to define structure in an open-ended, experimental project. Strong communication and documentation skills. Comfortable collaborating with subject-matter experts from engineering or industrial backgrounds. Curious, pragmatic, and able to balance innovation with execution. Success CriteriaWorking prototype demonstrating automated or semi-automated conversion of point-cloud data into structured engineering output. Documented and repeatable workflow that can be validated and scaled. Clear roadmap for technical enhancement and productization. Technology Stack (examples): Python | C++ | Open3D | PCL | PyTorch | TensorFlow | CloudCompare | AutoCAD / Revit APIs | IFC / DXF / DWG data structures | AWS / Azure / GCP