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 were 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).

Youre 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 façade engineers and delivery teams to:
  • Translate domain knowledge into useful guidelines, rules, and model objectives.
  • Validate that AI outputs are usable in real inspection workflows.
  • Establish a tight feedback loop that drives iterations and improvements.
• Work with cloud/infra engineers to specify training/inference requirements; they build the infrastructure, you ensure it meets model needs.
• Communicate clearly with leadership on progress, risks, hiring needs, and trade-offs.

Qualifications

Must-Have

• 5+ years hands-on ML/DL experience, with 3+ years in computer vision.
• Strong experience in image detection and segmentation using modern architectures (e.g., YOLO family, Mask R-CNN, UNet, transformer/ViT-based models).
• Proven track record taking CV models from prototype to production used in real projects and iterating based on monitoring + error analysis.
• Strong software engineering fundamentals:
  • Python, PyTorch/TensorFlow/JAX.
  • Clean, maintainable codebases; testing for critical paths; CI/CD literacy.
• Demonstrated ability to lead technically:
  • Youve been a senior reference point, reviewed others work, guided technical direction, and mentored engineers.
• Comfortable defining success metrics from ambiguity and defending trade-offs with data.
• Bias toward shipping and measurable customer impact over shiny research.

Nice-to-Have (Domain Fit)

• Experience with very high-resolution imagery (4060 MP) and large image volumes.
• Drone imagery, mapping, photogrammetry, or geospatial workflows.
• Building/infrastructure inspection, civil engineering, or similar domains.
• Familiarity with MLOps tooling (MLflow, W&B, SageMaker, Vertex, or equivalent).
• Experience building tooling/workflows for annotators, QA teams, or domain experts.

Job Benefits

What We Offer

• A fully remote position, allowing you to work from anywhere in the Philippines
• Competitive salary and benefits package (PTO and HMO)
• Employee Stock Ownership Plan (ESOP) eligibility
• Flexible working hours to accommodate project needs and time differences.
• Opportunities for professional growth and development in a company at the forefront of AI-driven building inspection technology.