Applied AI | Anomaly Detection Engineer

Mission

Build the automated pass/fail logic that turns raw deviation data into actionable QC decisions. Your anomaly detection models sit at the decision boundary – they determine whether a weld passes, a beam is misaligned, or a machined part is out of spec. This drives the upsell from “AR inspection viewer” to “AI-powered QC automation.”

System Ownership

• Primary: Anomaly detection models (deviation classification: pass / marginal / fail)
• Primary: Time-series quality trend analysis (degradation detection across inspection history)
• Primary: Automated QC decision engine (tolerance-aware pass/fail from 3D deviation data)
• Primary: Model training and evaluation pipeline (data prep, training, validation, A/B testing)
• Secondary interface: CAD Geometry Engineer (they provide GD&T tolerances and feature maps you evaluate against)
• Secondary interface: Backend Engineer (they store inspection data you train on and serve predictions through)
• Does NOT own: Point cloud registration (CV team), edge inference runtime (Edge AI team), dashboard visualisation (Full Stack team), data storage (Backend team)

What You Will Build

• Deviation classification model – Given a 3D deviation map (per-point distances between aligned scan and CAD reference), classify each region as pass, marginal, or fail. Account for GD&T tolerances: a 2mm deviation on a rough surface is pass; a 0.5mm deviation on a precision bore is fail.
• Anomaly detection for inspection time-series – Track deviation patterns across serial inspections. Detect: systematic drift (a machine tool wearing down), sudden shifts (fixture misalignment), seasonal patterns (thermal expansion in summer).
• Root cause suggestion engine – When an anomaly is detected, correlate with contextual data (operator, shift, machine, material batch, ambient temperature) to suggest probable root causes.
• Model training pipeline – Automated pipeline: data collection from inspection history → feature engineering → model training → offline evaluation → A/B test deployment → production rollout. MLOps-grade, not notebook-grade.
• Confidence scoring and explainability – Every pass/fail decision must include a confidence score and a spatial heatmap showing which regions drove the decision. Operators and QC managers need to understand and trust the output.
• Feedback loop integration – Operators can override AI decisions (mark false positives/negatives). These overrides feed back into training data with human-verified labels. Active learning loop.

Core Technical Responsibilities

• Design and train deviation classification models: input is per-point deviation vectors + feature type labels + GD&T tolerance bands → output is region-level pass / marginal / fail with confidence
• Build the time-series anomaly detection system using statistical methods (CUSUM, Shewhart charts) and ML methods (isolation forests, autoencoders, LSTM-based anomaly detectors) – compare and select based on real data
• Implement GD&T-aware evaluation: a deviation is not just a number – it must be interpreted against the specific tolerance for that feature type (flatness vs. concentricity vs. position vs. true position)
• Build the model training pipeline: data versioning (DVC), experiment tracking (MLflow or Weights & Biases), automated retraining triggers, model registry
• Implement model serving for both cloud and edge: cloud inference for batch analytics, ONNX/TFLite export for edge deployment
• Build the explainability layer: SHAP values or attention maps projected back onto 3D geometry for spatial explainability
• Design and run A/B tests: shadow mode deployment, statistical significance testing, automated rollback on regression

Required Technical Mastery

• Deep learning for industrial data: CNNs for 2D inspection images, PointNet/PointNet++ for 3D point clouds, autoencoders for unsupervised anomaly detection. Production deployment experience, not just paper reproduction
• Time-series and anomaly detection: Statistical process control (SPC), CUSUM, EWMA, isolation forests, LSTM autoencoders, variational autoencoders. Understanding of false positive / false negative tradeoffs in industrial QC
• MLOps: Model training pipelines (not notebooks), experiment tracking (MLflow, W&B), data versioning (DVC), model registry, CI/CD for models, automated retraining, A/B testing, canary deployments
• GD&T interpretation: ASME Y14.5 / ISO 1101 tolerance types (form, orientation, location, r…