AI Team Lead – Computer Vision and Structural Defect Detection

Position title

AI Team Lead – Computer Vision and Structural Defect Detection Description Inspekt  AI builds computer vision that replaces slow, manual façade inspections with scalable, repeatable, image-based workflows. We deploy models into real customer projects, and we’re now scaling model quality, training reliability, and AI-driven inspection throughput.

You will lead the AI engineering function, responsible for our core perception stack:
High-resolution façade defect detection, image quality filtering, segmentation/classification of façade components, and 2D/3D data fusion into inspection pipelines.

This is not a research-only role. Your work must ship to production, be measured in live inspections, and directly improve the quality and efficiency of our customer deliverables.
** Team stage & growth:
*** We are expanding the team quickly
* You will be the technical anchor and player-coach who sets direction, upgrades the training/data pipeline, and scales the team into a high-output AI function.
** Role split:*** **~80% hands-on technical work** (coding, experiments, architecture, model iteration, PR reviews).* **~20% technical leadership** (mentoring, standards, roadmap input, hiring support).You’re the person the team goes to when trade-offs are unclear, models underperform, or production behaves strangely

Responsibilities####
*
* 1) Hands-on AI / Computer Vision Delivery (~80%)
*** Design, implement, and improve production-grade CV models for façade inspection, including:  +
** Defect detection
** with precision-first targets.  +
** Segmentation/classification
** of façade components and materials.  +
** Image quality evaluation/filtering
** to improve downstream inspection accuracy and reduce noise.
* Own the
** end-to-end model lifecycle**:  + Data selection, preprocessing, and augmentation at scale (very high-res imagery, large project volumes).  + Labeling strategy and training-specific QA in collaboration with annotators and façade engineers.  + Training, validation, and evaluation using clear project-relevant metrics.  + Batch deployment to production pipelines and continuous monitoring/improvement.
* Build and maintain a systematic experimentation engine: hypotheses, baselines, ablations, and clear readouts of what worked and why.
* Write production-quality code: modular Python, robust training/inference components, tests for critical paths, and clean integration with internal services/APIs.####
*
* 2) Training Data Pipeline & Evaluation Foundations (Top Priority)
*** Establish a
** high-quality AI training data pipeline
** that is distinct from human annotation workflows, including:  + Dataset versioning and lineage.  + Sampling strategy and coverage guarantees across projects/building types.  + Label QA rules for training fitness (consistency, edge cases, class leakage).  + Repeatable train/val/test splits and regression tracking.
* Create repeatable error-analysis workflows and dashboards tied to real project outcomes.####
*
* 3) Technical Leadership & Mentoring (~20%)
*** Act as the go-to technical expert for AI engineers: unblock others on architecture, training stability, debugging, and performance issues.
* Set and enforce standards for AI engineering:  + Coding conventions, documentation, testing.  + Reproducibility and traceability for models and datasets.  + Experiment tracking discipline.
* Shape the AI roadmap with the technical management: recommend priorities based on impact, feasibility, and delivery constraints; clearly articulate trade-offs.
* Support hiring and onboarding of new AI engineers: interviews, technical assessment design, and structured onboarding to ramp quickly.####
*
* 4) Model Strategy & Architecture
*** Lead strategy for the
** model portfolio**:  + Decide when to use one generalized defect model vs. multiple specialized models (by building type, material, region, or inspection context).  + Define decision criteria and rollout plan, including how models are selected per project.
* Define and refine requirements for scalable training and inference architecture, ensuring reliability and cost-awareness.####
*
* 5) Collaboration & Stakeholder Management
*** Work with…